Introduction
Raw data
The raw data is shown below. There were 214 rows and 80 columns, consisting of 32 patients sampled over 7 timepoints. However, there is a significant amount of missing data, resulting in only 166 usable datapoints.
missing.ix <- Reduce(intersect, apply(df[,bcellcyto], 2, function(x) which(is.na(x))))
df.raw <- df[-missing.ix,]
df.raw <- df.raw[order(df.raw$PatientID),]
rownames(df.raw) <- 1:nrow(df.raw)
kable(df.raw,
digits = 3,
row.names = T,
caption = "Raw Data"
) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12) %>%
scroll_box(width = "100%", height = "300px")
Raw Data
|
|
PatientID
|
Time
|
Age
|
AgeGreater60
|
Sex
|
LowIntermacs
|
InterMACS
|
RVAD
|
Sensitized
|
VAD Indication
|
Device Type
|
Outcome
|
Survival
|
num Total PBMC
|
num lymph
|
lymph
|
live lymph
|
CD3 of live lymph
|
CD19 of live lymph
|
CD19+CD27-
|
CD19+CD27+
|
CD27+38++plasma blasts
|
CD27-38++ transitional
|
CD27-IgD+ mature naive
|
CD27+IgD- switched memory
|
CD27-IgD- switched memory
|
CD27+IgD+ unswitched memory
|
CD27+IgD-IgM+ switched memory
|
CD27+IgD+IgM+ nonswitched memory
|
CD19+27+IgG+IgM- memory
|
CD19+24dim38dim naive mature
|
CD19+24+38++transitional
|
CD19CD24hiCD38-memory
|
CD19+27-38+CD5+transitionals
|
CD19+CD268+
|
CD268 of +27-38++transitional
|
CD19+CD11b+
|
CD19+CD5+
|
CD19+CD27+CD24hi
|
CD19+CD5+CD24hi
|
CD19+CD5+CD11b+
|
CD19+27+IgD-38++IgG ASC
|
IL-12(p40)
|
IL-12(p70)
|
IFN-g
|
TNF-a
|
TNF-b
|
IL-4
|
IL-5
|
IL-9
|
IL-10
|
IL-13
|
IL-17A
|
IL-1a
|
IL-1b
|
IL-2
|
IL-3
|
IL-6
|
IL-15
|
TGF-a
|
IFN-a2
|
IL-8
|
GRO
|
Eotaxin
|
MDC
|
IP-10
|
MCP-1
|
MCP-3
|
Fractalkine
|
MIP-1a
|
MIP-1b
|
GM-CSF
|
IL-7
|
G-CSF
|
VEGF
|
EGF
|
FGF-2
|
Flt-3L
|
IL-1RA
|
sCD40L
|
|
1
|
1
|
0
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
169154
|
35496
|
20.98
|
99.53
|
25.37
|
19.82
|
86.02
|
13.98
|
1.26
|
2.54
|
57.81
|
11.74
|
28.06
|
2.39
|
21.74
|
14.36
|
0.72
|
81.05
|
2.86
|
13.60
|
0.58
|
95.22
|
84.83
|
10.47
|
4.33
|
8.50
|
1.39
|
3.13
|
1.09
|
1.71
|
2.16
|
124.000
|
20.926
|
2.72
|
2.500
|
1.010
|
7.273
|
4.617
|
1.760
|
21.20
|
277.000
|
3.483
|
9.878
|
1.160
|
12.334
|
1.090
|
5.71
|
2.18
|
45.38
|
126.00
|
119.000
|
525.00
|
1174
|
392
|
3.03
|
27.25
|
2.030
|
47.654
|
60.843
|
1.268
|
33.267
|
486.000
|
54.27
|
6.05
|
1.92
|
4.47
|
793.00
|
|
2
|
1
|
1
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
63082
|
9915
|
15.72
|
99.26
|
32.45
|
26.26
|
90.06
|
9.94
|
0.89
|
2.09
|
54.27
|
7.70
|
35.71
|
2.32
|
31.10
|
20.47
|
1.57
|
84.06
|
2.59
|
10.79
|
0.26
|
97.02
|
92.59
|
7.50
|
3.13
|
6.42
|
1.32
|
2.44
|
1.49
|
1.71
|
1.71
|
156.000
|
24.857
|
2.72
|
2.500
|
1.010
|
1.270
|
48.726
|
1.760
|
29.97
|
388.000
|
1.350
|
1.952
|
1.160
|
109.000
|
5.715
|
3.89
|
3.18
|
102.00
|
191.00
|
95.541
|
463.00
|
1079
|
552
|
3.03
|
35.53
|
2.030
|
62.089
|
12.293
|
2.005
|
109.000
|
509.000
|
64.48
|
26.13
|
1.92
|
19.50
|
841.00
|
|
3
|
1
|
3
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
75921
|
21721
|
28.61
|
99.31
|
24.86
|
33.01
|
86.38
|
13.62
|
1.54
|
1.26
|
45.74
|
10.98
|
40.44
|
2.84
|
31.80
|
18.04
|
1.15
|
86.21
|
4.82
|
6.56
|
0.24
|
90.41
|
73.33
|
10.00
|
7.25
|
10.12
|
3.22
|
5.67
|
2.05
|
32.32
|
14.40
|
259.000
|
28.330
|
14.74
|
12.770
|
3.369
|
5.615
|
28.055
|
7.406
|
54.61
|
438.000
|
3.867
|
8.638
|
3.690
|
57.616
|
12.778
|
5.85
|
56.84
|
121.00
|
193.00
|
186.000
|
510.00
|
1365
|
464
|
35.08
|
152.00
|
2.030
|
87.775
|
44.032
|
10.100
|
81.038
|
687.000
|
70.06
|
117.00
|
1.92
|
151.00
|
801.00
|
|
4
|
1
|
5
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
1.71
|
5.07
|
134.000
|
41.230
|
2.72
|
2.813
|
1.809
|
4.930
|
23.012
|
1.760
|
26.38
|
316.000
|
1.219
|
1.230
|
2.735
|
15.579
|
5.021
|
2.73
|
25.80
|
78.31
|
231.00
|
213.000
|
462.00
|
2053
|
583
|
4.14
|
104.00
|
2.030
|
55.375
|
49.222
|
6.820
|
29.004
|
414.000
|
61.03
|
43.25
|
1.92
|
64.51
|
1230.00
|
|
5
|
1
|
8
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
213808
|
36002
|
16.84
|
99.19
|
35.95
|
14.55
|
67.94
|
32.06
|
4.06
|
2.17
|
27.92
|
28.92
|
39.64
|
3.52
|
20.31
|
9.10
|
1.33
|
68.69
|
7.10
|
20.42
|
1.88
|
87.38
|
44.25
|
10.78
|
11.12
|
19.15
|
5.54
|
7.35
|
1.07
|
1.71
|
3.83
|
228.000
|
23.577
|
2.81
|
2.500
|
1.666
|
6.792
|
39.103
|
1.760
|
43.65
|
450.000
|
2.412
|
6.904
|
2.197
|
19.163
|
2.445
|
3.75
|
19.72
|
56.79
|
134.00
|
184.000
|
496.00
|
1214
|
430
|
3.03
|
90.07
|
2.030
|
72.549
|
44.681
|
4.448
|
51.520
|
735.000
|
78.74
|
94.11
|
1.92
|
133.00
|
912.00
|
|
6
|
1
|
21
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
106970
|
31867
|
29.79
|
99.18
|
42.36
|
15.02
|
84.69
|
15.31
|
1.94
|
2.11
|
55.41
|
13.14
|
29.13
|
2.32
|
20.22
|
12.88
|
2.08
|
83.21
|
5.20
|
9.50
|
1.10
|
94.44
|
73.00
|
9.04
|
6.99
|
9.22
|
2.80
|
4.78
|
1.44
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
7
|
2
|
0
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
47.38
|
62.54
|
800.000
|
44.293
|
47.38
|
58.846
|
142.000
|
8.004
|
27.135
|
23.250
|
152.00
|
1482.000
|
15.724
|
10.414
|
10.464
|
169.000
|
15.490
|
9.98
|
136.00
|
144.00
|
414.00
|
217.000
|
834.00
|
2701
|
464
|
74.28
|
328.00
|
44.782
|
220.000
|
47.925
|
53.810
|
185.000
|
1845.000
|
220.00
|
275.00
|
661.00
|
251.00
|
1300.00
|
|
8
|
2
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
1.71
|
108.00
|
305.000
|
26.686
|
2.72
|
2.500
|
24.287
|
1.270
|
40.482
|
1.760
|
92.32
|
396.000
|
0.858
|
1.230
|
1.228
|
177.000
|
13.937
|
8.89
|
16.74
|
122.00
|
86.72
|
106.000
|
470.00
|
1039
|
267
|
10.10
|
84.06
|
17.074
|
190.000
|
11.123
|
3.608
|
17.674
|
1146.000
|
145.00
|
311.00
|
365.00
|
583.00
|
1584.00
|
|
9
|
2
|
3
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
4.78
|
65.82
|
441.000
|
41.771
|
8.78
|
13.363
|
43.505
|
3.412
|
20.281
|
4.002
|
99.92
|
620.000
|
2.755
|
2.491
|
5.542
|
132.000
|
17.441
|
9.57
|
66.07
|
124.00
|
334.00
|
227.000
|
441.00
|
681
|
419
|
24.50
|
182.00
|
19.737
|
214.000
|
19.603
|
12.708
|
78.434
|
1314.000
|
146.00
|
311.00
|
439.00
|
251.00
|
1349.00
|
|
10
|
2
|
5
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
16.71
|
59.25
|
490.000
|
44.652
|
16.00
|
36.320
|
46.100
|
5.500
|
18.023
|
9.218
|
109.00
|
700.000
|
4.262
|
3.356
|
7.955
|
133.000
|
16.270
|
9.71
|
90.20
|
136.00
|
386.00
|
245.000
|
434.00
|
899
|
587
|
27.78
|
205.00
|
22.552
|
205.000
|
26.568
|
16.970
|
101.000
|
1464.000
|
153.00
|
361.00
|
469.00
|
205.00
|
1335.00
|
|
11
|
2
|
8
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
1.71
|
114.00
|
563.000
|
71.393
|
2.81
|
25.856
|
61.646
|
2.705
|
6.884
|
1.760
|
143.00
|
749.000
|
2.086
|
4.590
|
2.553
|
148.000
|
9.354
|
10.52
|
41.30
|
140.00
|
249.00
|
258.000
|
479.00
|
1372
|
764
|
26.17
|
223.00
|
34.562
|
258.000
|
25.928
|
6.167
|
59.758
|
1701.000
|
159.00
|
489.00
|
525.00
|
263.00
|
801.00
|
|
12
|
3
|
0
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
119377
|
77509
|
64.93
|
99.61
|
58.50
|
7.99
|
62.67
|
37.33
|
9.10
|
4.82
|
36.73
|
31.90
|
25.62
|
5.74
|
11.47
|
8.95
|
0.40
|
69.54
|
7.69
|
9.41
|
2.61
|
76.44
|
57.91
|
23.29
|
20.76
|
15.39
|
5.42
|
14.90
|
2.60
|
6.39
|
6.65
|
11.372
|
21.497
|
3.51
|
2.320
|
6.303
|
1.590
|
6.039
|
2.111
|
5.18
|
4.727
|
1.920
|
1.450
|
2.558
|
38.074
|
5.729
|
2.57
|
19.65
|
32.02
|
123.00
|
133.000
|
380.00
|
1899
|
1054
|
26.14
|
112.00
|
2.230
|
32.026
|
20.483
|
7.783
|
58.902
|
70.513
|
7.02
|
95.30
|
0.39
|
89.23
|
624.00
|
|
13
|
3
|
1
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
41.70
|
5.35
|
39.418
|
40.838
|
13.66
|
17.416
|
2.457
|
4.050
|
57.842
|
9.006
|
7.14
|
46.122
|
4.879
|
6.666
|
3.299
|
119.000
|
26.234
|
2.86
|
67.14
|
112.00
|
206.00
|
180.000
|
411.00
|
1271
|
4441
|
35.46
|
169.00
|
33.419
|
100.000
|
54.262
|
5.253
|
43.646
|
118.000
|
24.49
|
79.63
|
0.39
|
224.00
|
1714.00
|
|
14
|
3
|
3
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
96940
|
57658
|
59.48
|
99.70
|
55.52
|
9.21
|
72.15
|
27.85
|
3.85
|
3.82
|
48.41
|
21.75
|
23.49
|
6.35
|
12.10
|
16.04
|
0.28
|
75.30
|
3.72
|
6.65
|
2.11
|
80.52
|
67.33
|
15.80
|
15.23
|
6.71
|
1.32
|
8.86
|
0.26
|
17.15
|
6.00
|
20.672
|
15.342
|
7.00
|
5.509
|
7.681
|
2.040
|
8.932
|
3.799
|
4.45
|
17.129
|
2.699
|
3.725
|
3.108
|
25.794
|
15.326
|
2.57
|
74.20
|
34.62
|
85.04
|
162.000
|
348.00
|
897
|
1361
|
21.65
|
102.00
|
6.821
|
27.057
|
24.672
|
5.253
|
34.257
|
70.513
|
13.56
|
63.96
|
0.39
|
118.00
|
1014.00
|
|
15
|
3
|
5
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
154373
|
73969
|
47.92
|
99.65
|
45.18
|
8.77
|
67.36
|
32.64
|
6.59
|
2.77
|
39.10
|
26.28
|
27.95
|
6.67
|
17.89
|
15.71
|
0.39
|
71.57
|
6.26
|
9.88
|
1.78
|
81.86
|
72.07
|
20.12
|
20.17
|
14.80
|
5.99
|
14.65
|
3.71
|
4.28
|
2.29
|
12.657
|
16.490
|
2.31
|
2.320
|
6.816
|
1.590
|
6.845
|
1.520
|
4.74
|
2.120
|
1.920
|
1.450
|
1.980
|
99.474
|
11.334
|
1.58
|
47.29
|
61.82
|
48.64
|
192.000
|
320.00
|
738
|
1071
|
10.05
|
46.41
|
2.230
|
21.777
|
11.020
|
1.520
|
37.403
|
27.750
|
2.89
|
23.91
|
0.39
|
52.69
|
361.00
|
|
16
|
3
|
8
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
155610
|
63375
|
40.73
|
99.51
|
38.42
|
12.40
|
71.69
|
28.31
|
4.36
|
2.62
|
37.82
|
23.43
|
33.50
|
5.24
|
17.37
|
14.92
|
0.60
|
65.04
|
2.80
|
11.23
|
2.54
|
83.55
|
37.56
|
15.55
|
15.45
|
9.55
|
2.66
|
10.91
|
0.28
|
8.54
|
4.12
|
17.955
|
15.342
|
3.51
|
2.320
|
6.816
|
1.590
|
6.439
|
1.605
|
5.62
|
13.939
|
2.314
|
1.782
|
2.042
|
79.869
|
8.070
|
2.42
|
84.63
|
34.26
|
38.88
|
162.000
|
335.00
|
822
|
1054
|
16.46
|
84.38
|
2.980
|
19.937
|
20.483
|
2.734
|
21.566
|
90.169
|
10.50
|
41.25
|
0.39
|
77.29
|
562.00
|
|
17
|
3
|
14
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
244245
|
115109
|
47.13
|
99.47
|
47.40
|
9.37
|
67.50
|
32.50
|
3.99
|
2.08
|
50.08
|
26.01
|
17.22
|
6.68
|
15.34
|
15.25
|
0.23
|
77.86
|
3.21
|
10.49
|
1.48
|
82.13
|
59.19
|
21.45
|
19.77
|
14.45
|
2.57
|
14.94
|
0.94
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
18
|
4
|
1
|
58
|
younger
|
Male
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
246811
|
86529
|
35.06
|
93.49
|
67.96
|
17.48
|
81.66
|
18.34
|
2.05
|
6.85
|
51.33
|
13.69
|
29.91
|
5.08
|
15.68
|
5.94
|
0.19
|
61.95
|
0.76
|
13.51
|
4.07
|
92.62
|
34.06
|
6.90
|
12.07
|
8.00
|
2.54
|
3.85
|
0.26
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
19
|
4
|
3
|
58
|
younger
|
Male
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
406771
|
90343
|
22.21
|
97.34
|
49.28
|
17.02
|
75.04
|
24.96
|
4.47
|
18.36
|
28.45
|
21.86
|
46.07
|
3.62
|
24.97
|
4.33
|
0.13
|
47.41
|
1.64
|
19.70
|
18.15
|
72.76
|
8.08
|
25.69
|
28.35
|
13.95
|
4.66
|
19.20
|
0.41
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
20
|
4
|
21
|
58
|
younger
|
Male
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
330191
|
81636
|
24.72
|
94.57
|
45.14
|
14.70
|
70.51
|
29.49
|
8.94
|
16.23
|
38.92
|
23.53
|
30.83
|
6.72
|
23.93
|
7.07
|
0.18
|
46.63
|
2.35
|
16.12
|
8.59
|
74.45
|
39.90
|
24.19
|
21.18
|
12.62
|
3.26
|
12.93
|
0.38
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
21
|
5
|
0
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
41.24
|
109.00
|
187.000
|
25.955
|
53.99
|
42.552
|
10.747
|
12.567
|
51.239
|
17.031
|
68.25
|
64.203
|
7.364
|
13.383
|
10.807
|
70.212
|
17.104
|
10.88
|
161.00
|
95.72
|
536.00
|
281.000
|
2294.00
|
624
|
929
|
24.90
|
228.00
|
22.644
|
85.566
|
58.104
|
17.699
|
193.000
|
1151.000
|
107.00
|
183.00
|
41.52
|
377.00
|
6509.00
|
|
22
|
5
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
25.64
|
18.30
|
67.494
|
20.261
|
8.29
|
11.301
|
2.861
|
4.489
|
59.406
|
3.964
|
20.30
|
20.414
|
1.860
|
4.133
|
3.291
|
211.000
|
13.360
|
3.81
|
94.97
|
78.63
|
197.00
|
81.039
|
880.00
|
512
|
1398
|
16.57
|
80.75
|
13.454
|
68.452
|
36.398
|
4.775
|
71.797
|
376.000
|
23.81
|
173.00
|
19.21
|
128.00
|
2712.00
|
|
23
|
5
|
3
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
65.91
|
44.73
|
94.077
|
18.237
|
66.52
|
41.525
|
10.572
|
11.269
|
39.553
|
17.428
|
29.48
|
72.616
|
8.804
|
10.630
|
17.422
|
203.000
|
21.250
|
8.49
|
159.00
|
54.16
|
674.00
|
230.000
|
562.00
|
201
|
607
|
37.07
|
181.00
|
13.404
|
67.666
|
56.614
|
14.252
|
152.000
|
646.000
|
78.65
|
138.00
|
26.01
|
319.00
|
10443.75
|
|
24
|
5
|
5
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
45.56
|
41.78
|
173.000
|
17.642
|
45.32
|
43.807
|
7.681
|
8.007
|
29.730
|
15.356
|
50.08
|
131.000
|
8.403
|
10.203
|
12.135
|
96.449
|
20.663
|
8.39
|
173.00
|
74.75
|
989.00
|
349.000
|
615.00
|
223
|
507
|
35.46
|
195.00
|
16.773
|
97.826
|
50.067
|
27.582
|
147.000
|
859.000
|
103.00
|
301.00
|
12.72
|
199.00
|
9557.42
|
|
25
|
5
|
8
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
46.23
|
76.19
|
129.000
|
19.211
|
64.75
|
40.499
|
12.238
|
12.567
|
41.854
|
15.844
|
57.83
|
87.429
|
9.848
|
14.449
|
16.809
|
92.658
|
17.104
|
9.86
|
177.00
|
68.47
|
747.00
|
184.000
|
709.00
|
491
|
642
|
34.12
|
181.00
|
16.173
|
77.694
|
62.554
|
17.001
|
163.000
|
790.000
|
101.00
|
212.00
|
30.50
|
325.00
|
10443.75
|
|
26
|
6
|
0
|
67
|
older
|
Male
|
Low
|
3
|
No
|
Yes
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
309890
|
41896
|
13.52
|
97.03
|
47.13
|
8.08
|
77.31
|
22.69
|
2.74
|
3.29
|
59.03
|
19.80
|
19.37
|
1.80
|
21.10
|
4.46
|
19.79
|
71.98
|
2.41
|
17.39
|
0.20
|
95.80
|
75.00
|
5.79
|
1.89
|
16.45
|
0.91
|
0.67
|
0.77
|
4.28
|
4.73
|
52.585
|
19.181
|
5.78
|
2.320
|
1.758
|
1.590
|
16.952
|
1.605
|
8.23
|
2.120
|
1.920
|
1.450
|
2.382
|
38.750
|
1.342
|
12.51
|
27.01
|
37.91
|
534.00
|
147.000
|
353.00
|
425
|
293
|
21.65
|
77.95
|
6.139
|
44.075
|
11.020
|
5.888
|
46.738
|
153.000
|
45.46
|
128.00
|
0.39
|
129.00
|
2818.00
|
|
27
|
6
|
1
|
67
|
older
|
Male
|
Low
|
3
|
No
|
Yes
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
274228
|
15653
|
5.71
|
97.85
|
35.76
|
28.87
|
51.45
|
48.55
|
2.60
|
2.13
|
30.85
|
43.13
|
22.77
|
3.26
|
20.09
|
3.92
|
30.51
|
57.58
|
1.31
|
28.20
|
0.18
|
91.23
|
59.57
|
12.08
|
2.04
|
34.44
|
0.38
|
0.16
|
0.16
|
2.18
|
4.12
|
60.794
|
37.589
|
2.31
|
2.320
|
1.125
|
1.590
|
80.735
|
1.520
|
12.24
|
2.120
|
1.920
|
1.450
|
1.980
|
173.000
|
3.201
|
15.43
|
16.01
|
121.00
|
231.00
|
210.000
|
265.00
|
359
|
515
|
3.98
|
77.95
|
48.595
|
128.000
|
6.036
|
3.987
|
52.863
|
60.307
|
7.02
|
55.00
|
0.39
|
65.11
|
1125.00
|
|
28
|
6
|
3
|
67
|
older
|
Male
|
Low
|
3
|
No
|
Yes
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
591939
|
57589
|
9.73
|
96.76
|
38.20
|
42.54
|
75.71
|
24.29
|
2.14
|
1.33
|
53.03
|
21.26
|
24.16
|
1.55
|
25.50
|
4.02
|
20.27
|
67.18
|
1.14
|
21.71
|
0.11
|
95.28
|
66.98
|
3.79
|
2.43
|
17.70
|
0.65
|
0.84
|
0.18
|
2.18
|
5.35
|
60.114
|
36.632
|
2.49
|
2.320
|
1.496
|
1.590
|
48.963
|
1.520
|
11.92
|
2.120
|
1.920
|
1.450
|
2.210
|
171.000
|
3.894
|
13.78
|
38.10
|
123.00
|
342.00
|
282.000
|
313.00
|
467
|
727
|
10.05
|
96.17
|
8.166
|
75.587
|
16.356
|
5.888
|
122.000
|
99.662
|
15.43
|
66.76
|
0.39
|
124.00
|
1448.00
|
|
29
|
6
|
5
|
67
|
older
|
Male
|
Low
|
3
|
No
|
Yes
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
189634
|
46711
|
24.63
|
97.74
|
34.84
|
37.32
|
75.30
|
24.70
|
0.70
|
0.55
|
46.97
|
22.05
|
30.28
|
0.70
|
23.67
|
1.56
|
31.44
|
73.33
|
0.75
|
18.59
|
0.00
|
84.61
|
86.17
|
3.44
|
2.11
|
18.68
|
0.94
|
0.63
|
0.69
|
2.22
|
8.01
|
26.182
|
21.497
|
5.78
|
4.359
|
2.457
|
1.686
|
6.039
|
3.799
|
5.33
|
2.120
|
1.920
|
1.450
|
3.299
|
16.068
|
1.623
|
2.42
|
41.79
|
27.18
|
322.00
|
227.000
|
215.00
|
723
|
377
|
13.47
|
102.00
|
2.713
|
38.246
|
13.657
|
7.783
|
43.646
|
118.000
|
22.35
|
66.76
|
0.39
|
83.29
|
2184.00
|
|
30
|
6
|
8
|
67
|
older
|
Male
|
Low
|
3
|
No
|
Yes
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
10.69
|
8.70
|
66.212
|
75.794
|
2.49
|
5.509
|
1.758
|
1.601
|
8.508
|
1.520
|
13.55
|
2.120
|
1.920
|
1.450
|
3.108
|
53.482
|
1.623
|
9.74
|
52.76
|
108.00
|
224.00
|
235.000
|
349.00
|
1005
|
510
|
19.17
|
117.00
|
162.000
|
146.000
|
19.099
|
13.310
|
43.646
|
99.662
|
11.56
|
86.63
|
0.39
|
112.00
|
1595.00
|
|
31
|
6
|
14
|
67
|
older
|
Male
|
Low
|
3
|
No
|
Yes
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
182319
|
40475
|
22.20
|
96.28
|
43.36
|
11.77
|
69.34
|
30.66
|
1.59
|
0.89
|
52.64
|
27.32
|
18.56
|
1.48
|
22.79
|
2.88
|
28.61
|
75.47
|
0.33
|
14.54
|
0.06
|
95.90
|
53.66
|
7.81
|
3.27
|
21.04
|
1.57
|
1.50
|
0.72
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
32
|
7
|
0
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
1073919
|
336538
|
31.34
|
100.00
|
63.29
|
14.80
|
93.05
|
6.95
|
0.73
|
8.05
|
75.49
|
4.55
|
19.16
|
0.80
|
7.25
|
8.78
|
28.04
|
88.72
|
2.51
|
5.18
|
1.02
|
90.49
|
68.18
|
2.32
|
4.09
|
1.69
|
3.48
|
1.27
|
5.78
|
103.00
|
34.40
|
73.796
|
17.487
|
330.00
|
74.260
|
18.401
|
16.683
|
51.466
|
82.515
|
12.80
|
150.000
|
15.178
|
15.208
|
9.970
|
26.235
|
21.107
|
8.44
|
133.00
|
39.26
|
415.00
|
111.000
|
298.00
|
443
|
296
|
138.00
|
125.00
|
10.503
|
37.240
|
39.468
|
13.629
|
111.000
|
409.000
|
77.43
|
78.89
|
10.95
|
301.00
|
5998.00
|
|
33
|
7
|
1
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
270301
|
97229
|
35.97
|
91.63
|
18.43
|
61.67
|
97.68
|
2.32
|
0.20
|
5.85
|
69.45
|
1.63
|
28.19
|
0.73
|
27.57
|
21.43
|
17.05
|
84.32
|
7.26
|
2.73
|
0.84
|
99.44
|
97.60
|
0.54
|
2.64
|
1.51
|
3.63
|
0.29
|
3.40
|
16.38
|
6.78
|
46.507
|
34.398
|
28.79
|
11.752
|
0.814
|
4.999
|
1481.000
|
8.712
|
8.92
|
17.769
|
2.325
|
4.279
|
2.360
|
63.109
|
16.386
|
5.90
|
34.66
|
152.00
|
295.00
|
55.059
|
278.00
|
1037
|
409
|
21.48
|
40.32
|
6.177
|
30.898
|
20.452
|
2.676
|
177.000
|
224.000
|
36.40
|
47.22
|
0.92
|
160.00
|
5604.00
|
|
34
|
7
|
3
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
499016
|
78019
|
15.63
|
89.41
|
25.83
|
51.38
|
92.98
|
7.02
|
0.32
|
4.66
|
51.34
|
6.31
|
41.59
|
0.76
|
36.49
|
5.96
|
22.81
|
73.48
|
6.73
|
7.93
|
0.33
|
98.78
|
96.52
|
1.31
|
2.06
|
5.19
|
2.68
|
0.59
|
2.45
|
16.38
|
6.52
|
31.962
|
18.537
|
34.49
|
16.410
|
2.113
|
4.287
|
78.861
|
9.272
|
9.04
|
17.769
|
2.902
|
2.861
|
3.595
|
11.983
|
22.669
|
9.71
|
14.43
|
38.26
|
183.00
|
56.470
|
219.00
|
307
|
219
|
29.76
|
47.34
|
5.053
|
3.891
|
7.570
|
2.878
|
33.464
|
191.000
|
16.92
|
81.03
|
0.92
|
99.54
|
4583.00
|
|
35
|
7
|
8
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
115825
|
32513
|
28.07
|
91.04
|
44.61
|
15.76
|
80.52
|
19.48
|
4.16
|
7.82
|
49.91
|
18.37
|
30.52
|
1.20
|
14.87
|
3.33
|
30.19
|
61.08
|
8.70
|
12.56
|
1.58
|
89.80
|
76.71
|
2.87
|
6.07
|
11.12
|
8.08
|
1.59
|
1.17
|
37.61
|
21.91
|
70.846
|
24.832
|
57.61
|
38.448
|
5.988
|
8.685
|
44.610
|
13.488
|
17.95
|
55.857
|
7.772
|
7.570
|
7.238
|
14.443
|
23.376
|
10.40
|
81.88
|
77.46
|
332.00
|
132.000
|
332.00
|
1453
|
632
|
38.47
|
104.00
|
12.646
|
40.206
|
31.261
|
7.509
|
79.659
|
354.000
|
26.71
|
180.00
|
0.92
|
274.00
|
7156.00
|
|
36
|
7
|
21
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
407080
|
97651
|
23.99
|
91.29
|
66.96
|
12.20
|
84.66
|
15.34
|
2.88
|
5.60
|
61.89
|
13.73
|
22.65
|
1.74
|
20.16
|
7.23
|
29.39
|
68.49
|
5.28
|
10.60
|
1.17
|
92.50
|
54.35
|
3.48
|
6.53
|
9.61
|
8.34
|
1.85
|
2.83
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
37
|
8
|
0
|
43
|
younger
|
Male
|
High
|
2
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
515760
|
266115
|
51.60
|
96.30
|
69.66
|
8.57
|
72.22
|
27.78
|
6.26
|
4.77
|
43.09
|
26.01
|
28.26
|
2.63
|
16.67
|
2.67
|
28.25
|
50.35
|
0.41
|
12.78
|
4.47
|
84.76
|
19.47
|
9.56
|
4.55
|
8.29
|
0.87
|
2.18
|
0.75
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
38
|
8
|
8
|
43
|
younger
|
Male
|
High
|
2
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
539521
|
223872
|
41.49
|
96.72
|
60.02
|
12.63
|
80.13
|
19.87
|
1.66
|
2.48
|
43.56
|
19.52
|
35.24
|
1.68
|
20.46
|
3.67
|
20.54
|
55.76
|
0.18
|
10.80
|
2.43
|
87.82
|
13.25
|
6.55
|
5.96
|
4.65
|
0.95
|
2.51
|
0.65
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
39
|
8
|
21
|
43
|
younger
|
Male
|
High
|
2
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
564317
|
216312
|
38.33
|
97.40
|
55.80
|
4.83
|
74.19
|
25.81
|
5.35
|
5.77
|
37.90
|
25.72
|
34.76
|
1.62
|
17.99
|
2.18
|
31.75
|
61.61
|
0.77
|
13.44
|
6.97
|
76.55
|
6.97
|
10.34
|
11.14
|
4.46
|
0.94
|
5.14
|
2.43
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
40
|
9
|
0
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
89251
|
65181
|
73.03
|
76.76
|
84.53
|
1.15
|
74.65
|
25.35
|
1.91
|
1.39
|
55.03
|
22.40
|
19.62
|
2.95
|
15.65
|
6.80
|
25.85
|
70.49
|
0.87
|
23.26
|
0.23
|
83.16
|
25.00
|
8.85
|
5.03
|
11.63
|
1.56
|
2.95
|
3.08
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
41
|
9
|
3
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
375167
|
234817
|
62.59
|
98.46
|
60.29
|
10.18
|
87.72
|
12.28
|
0.46
|
0.48
|
68.54
|
9.90
|
19.03
|
2.54
|
30.58
|
19.69
|
15.54
|
72.77
|
0.13
|
20.35
|
0.03
|
92.56
|
36.28
|
3.75
|
2.70
|
9.17
|
1.19
|
1.27
|
0.63
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
42
|
9
|
5
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
256306
|
162176
|
63.27
|
93.82
|
68.43
|
8.45
|
85.45
|
14.55
|
0.70
|
0.53
|
71.99
|
11.98
|
13.37
|
2.66
|
32.41
|
17.96
|
12.46
|
69.74
|
0.41
|
24.63
|
0.02
|
95.72
|
44.12
|
4.47
|
2.23
|
12.99
|
1.30
|
1.20
|
0.77
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
43
|
9
|
8
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HVAD
|
Alive s/p OHT
|
alive
|
463136
|
330714
|
71.41
|
98.79
|
70.51
|
7.30
|
87.51
|
12.49
|
1.78
|
0.58
|
69.60
|
10.61
|
17.82
|
1.97
|
29.83
|
14.55
|
12.59
|
75.03
|
0.15
|
21.61
|
0.07
|
93.59
|
30.94
|
4.05
|
3.07
|
10.00
|
1.08
|
1.35
|
0.62
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
44
|
10
|
0
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
92221
|
27988
|
30.35
|
85.45
|
55.78
|
1.51
|
85.56
|
14.44
|
0.28
|
0.28
|
55.28
|
4.72
|
39.72
|
0.28
|
0.00
|
1.89
|
1.89
|
89.44
|
0.00
|
2.78
|
0.00
|
15.00
|
0.00
|
3.33
|
0.83
|
0.28
|
0.28
|
3.33
|
0.00
|
7.64
|
8.05
|
12.160
|
18.210
|
2.65
|
2.810
|
2.640
|
2.960
|
12.830
|
1.500
|
2.82
|
2.930
|
1.340
|
0.600
|
2.050
|
88.780
|
4.370
|
4.15
|
28.83
|
39.26
|
873.00
|
84.790
|
386.00
|
413
|
361
|
15.64
|
102.00
|
3.080
|
25.630
|
12.440
|
3.660
|
40.120
|
112.000
|
84.03
|
48.41
|
2.75
|
67.22
|
13502.00
|
|
45
|
10
|
1
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
273746
|
46755
|
17.08
|
93.25
|
50.14
|
7.57
|
88.27
|
11.73
|
0.09
|
2.12
|
54.52
|
3.36
|
42.00
|
0.12
|
8.40
|
6.87
|
0.00
|
92.00
|
0.12
|
3.45
|
0.00
|
3.94
|
11.43
|
1.12
|
0.64
|
2.76
|
0.33
|
2.15
|
0.00
|
1.74
|
3.20
|
8.390
|
13.870
|
2.65
|
2.810
|
2.640
|
2.960
|
5.480
|
1.500
|
2.63
|
1.040
|
0.420
|
0.020
|
0.960
|
56.230
|
6.110
|
2.78
|
12.80
|
22.38
|
257.00
|
109.000
|
212.00
|
207
|
352
|
2.90
|
59.32
|
3.080
|
10.480
|
4.350
|
2.300
|
35.160
|
31.280
|
16.68
|
40.19
|
1.22
|
37.18
|
3646.00
|
|
46
|
10
|
3
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
156053
|
71752
|
45.98
|
78.38
|
35.43
|
27.12
|
78.57
|
21.43
|
0.24
|
0.60
|
58.56
|
5.80
|
18.33
|
17.31
|
12.67
|
72.96
|
0.53
|
94.06
|
0.49
|
5.11
|
0.28
|
22.79
|
27.47
|
0.81
|
5.53
|
0.01
|
0.29
|
0.19
|
0.00
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
47
|
10
|
5
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
28.26
|
29.24
|
25.930
|
19.130
|
12.01
|
9.860
|
4.910
|
2.960
|
12.270
|
2.300
|
8.31
|
8.480
|
4.420
|
6.460
|
5.600
|
31.690
|
12.300
|
6.41
|
65.76
|
22.02
|
605.00
|
111.000
|
650.00
|
279
|
320
|
27.98
|
245.00
|
8.330
|
34.600
|
26.450
|
10.410
|
112.000
|
184.000
|
48.97
|
197.00
|
24.56
|
128.00
|
8387.00
|
|
48
|
10
|
8
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
156053
|
71752
|
45.98
|
78.38
|
35.43
|
27.12
|
78.57
|
21.43
|
0.24
|
0.60
|
58.56
|
5.80
|
18.33
|
17.31
|
12.67
|
72.96
|
0.53
|
94.06
|
0.49
|
5.11
|
0.28
|
22.79
|
27.47
|
0.81
|
5.53
|
0.01
|
0.29
|
0.19
|
0.00
|
70.58
|
65.90
|
45.390
|
26.340
|
33.60
|
24.140
|
10.790
|
5.940
|
18.220
|
4.580
|
16.86
|
22.040
|
10.800
|
17.200
|
14.210
|
33.990
|
19.940
|
11.61
|
146.00
|
23.83
|
661.00
|
105.000
|
765.00
|
325
|
270
|
45.41
|
414.00
|
17.810
|
51.840
|
49.970
|
22.030
|
205.000
|
266.000
|
76.21
|
266.00
|
48.57
|
276.00
|
9997.00
|
|
49
|
11
|
3
|
60
|
younger
|
Female
|
High
|
2
|
Yes
|
Yes
|
BTT
|
PVAD
|
Died
|
dead
|
116462
|
41076
|
35.27
|
95.44
|
33.79
|
25.24
|
84.51
|
15.49
|
2.92
|
0.99
|
45.34
|
13.19
|
39.12
|
2.36
|
17.56
|
8.75
|
19.73
|
79.35
|
5.17
|
10.80
|
0.71
|
90.04
|
29.59
|
10.16
|
6.81
|
8.21
|
3.37
|
4.86
|
4.47
|
16.71
|
5.07
|
550.000
|
96.245
|
2.72
|
2.500
|
6.486
|
1.361
|
62.319
|
3.478
|
19.00
|
32.659
|
1.350
|
3.356
|
1.533
|
254.000
|
17.441
|
6.13
|
35.07
|
87.82
|
55.52
|
228.000
|
423.00
|
7084
|
1125
|
10.10
|
84.06
|
30.293
|
26.765
|
20.856
|
5.302
|
78.434
|
153.000
|
3.21
|
132.00
|
1.92
|
103.00
|
710.00
|
|
50
|
11
|
5
|
60
|
younger
|
Female
|
High
|
2
|
Yes
|
Yes
|
BTT
|
PVAD
|
Died
|
dead
|
222650
|
85649
|
38.47
|
98.40
|
32.09
|
19.82
|
77.54
|
22.46
|
5.02
|
1.10
|
43.96
|
19.20
|
33.50
|
3.34
|
17.47
|
9.77
|
15.63
|
71.89
|
6.68
|
14.08
|
0.70
|
86.49
|
27.87
|
13.04
|
9.84
|
12.30
|
4.35
|
7.98
|
6.39
|
9.78
|
8.45
|
727.000
|
112.000
|
2.72
|
2.500
|
7.387
|
1.361
|
70.374
|
2.483
|
35.08
|
18.389
|
1.350
|
3.356
|
1.378
|
367.000
|
16.270
|
7.93
|
41.30
|
108.00
|
183.00
|
241.000
|
526.00
|
5632
|
1295
|
12.58
|
77.86
|
46.047
|
25.264
|
23.381
|
4.874
|
96.343
|
142.000
|
4.47
|
197.00
|
1.92
|
79.15
|
769.00
|
|
51
|
11
|
8
|
60
|
younger
|
Female
|
High
|
2
|
Yes
|
Yes
|
BTT
|
PVAD
|
Died
|
dead
|
102274
|
63057
|
61.65
|
97.73
|
27.89
|
20.66
|
73.75
|
26.25
|
7.33
|
3.45
|
35.79
|
21.92
|
37.73
|
4.56
|
9.97
|
8.20
|
16.96
|
63.96
|
2.40
|
11.16
|
3.07
|
73.97
|
5.92
|
16.69
|
17.77
|
11.74
|
4.90
|
12.19
|
6.09
|
8.08
|
5.07
|
592.000
|
95.220
|
2.81
|
2.500
|
9.032
|
1.270
|
88.002
|
2.970
|
30.12
|
13.849
|
1.350
|
2.217
|
1.612
|
342.000
|
16.660
|
5.71
|
31.97
|
90.34
|
159.00
|
194.000
|
472.00
|
4304
|
1610
|
4.14
|
95.92
|
35.248
|
54.219
|
22.115
|
4.660
|
88.759
|
110.000
|
2.67
|
143.00
|
1.92
|
115.00
|
702.00
|
|
52
|
11
|
14
|
60
|
younger
|
Female
|
High
|
2
|
Yes
|
Yes
|
BTT
|
PVAD
|
Died
|
dead
|
133530
|
81665
|
61.16
|
96.46
|
22.03
|
16.15
|
35.30
|
64.70
|
13.74
|
2.33
|
8.48
|
52.28
|
26.37
|
12.87
|
5.29
|
5.79
|
19.15
|
47.48
|
5.65
|
17.83
|
5.43
|
39.88
|
6.08
|
52.08
|
62.11
|
37.45
|
21.95
|
50.15
|
3.84
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
53
|
12
|
0
|
36
|
younger
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Alive s/p OHT
|
alive
|
481928
|
162052
|
33.63
|
99.70
|
64.73
|
5.05
|
3.91
|
96.09
|
8.89
|
1.34
|
0.72
|
94.99
|
3.14
|
1.15
|
4.07
|
0.84
|
0.32
|
90.28
|
2.84
|
1.58
|
1.27
|
6.82
|
9.17
|
14.61
|
90.11
|
4.95
|
5.71
|
13.01
|
0.51
|
13.27
|
17.11
|
204.000
|
22.584
|
83.74
|
2.970
|
7.222
|
1.989
|
113.000
|
2.290
|
35.01
|
208.000
|
1.165
|
5.160
|
2.360
|
23.895
|
10.191
|
3.42
|
54.53
|
61.90
|
250.00
|
205.000
|
697.00
|
388
|
201
|
40.18
|
50.81
|
11.088
|
68.713
|
35.373
|
1.364
|
48.096
|
767.000
|
84.43
|
69.91
|
190.00
|
798.00
|
3051.00
|
|
54
|
12
|
1
|
36
|
younger
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.77
|
9.96
|
64.103
|
28.423
|
5.76
|
2.970
|
0.420
|
1.989
|
477.000
|
2.290
|
13.19
|
3.190
|
0.940
|
0.850
|
2.360
|
85.133
|
16.242
|
2.59
|
30.46
|
156.00
|
455.00
|
67.295
|
507.00
|
225
|
483
|
19.92
|
62.77
|
6.118
|
35.321
|
8.503
|
1.901
|
237.000
|
258.000
|
31.01
|
36.77
|
18.08
|
131.00
|
4568.00
|
|
55
|
12
|
3
|
36
|
younger
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
43.64
|
27.96
|
177.000
|
51.350
|
85.07
|
5.509
|
4.877
|
4.159
|
33.230
|
9.696
|
23.76
|
162.000
|
3.963
|
10.605
|
3.492
|
26.609
|
14.056
|
6.09
|
99.90
|
81.00
|
1152.00
|
204.000
|
307.00
|
854
|
1019
|
63.53
|
142.00
|
23.341
|
95.130
|
65.342
|
9.657
|
93.343
|
633.000
|
79.44
|
262.00
|
147.00
|
284.00
|
5911.00
|
|
56
|
12
|
5
|
36
|
younger
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
7.16
|
6.78
|
8.658
|
17.038
|
2.80
|
2.970
|
1.553
|
1.231
|
28.927
|
3.330
|
3.48
|
4.260
|
1.165
|
2.060
|
2.360
|
30.095
|
2.770
|
5.31
|
49.41
|
39.91
|
376.00
|
64.661
|
398.00
|
334
|
344
|
20.45
|
40.32
|
3.050
|
12.574
|
11.877
|
1.901
|
18.625
|
158.000
|
40.18
|
183.00
|
0.92
|
74.91
|
10443.75
|
|
57
|
12
|
8
|
36
|
younger
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Alive s/p OHT
|
alive
|
38113
|
21486
|
56.37
|
97.86
|
58.44
|
7.88
|
20.47
|
79.53
|
8.09
|
1.69
|
1.21
|
75.00
|
18.96
|
4.83
|
10.07
|
2.59
|
0.53
|
82.19
|
2.29
|
9.00
|
3.59
|
15.16
|
0.00
|
23.07
|
63.95
|
4.77
|
6.52
|
16.91
|
0.40
|
7.16
|
9.15
|
39.545
|
15.389
|
5.76
|
2.970
|
1.399
|
1.814
|
11.147
|
2.723
|
8.55
|
7.477
|
1.430
|
0.850
|
2.360
|
15.487
|
3.399
|
2.59
|
37.42
|
29.78
|
548.00
|
107.000
|
502.00
|
225
|
445
|
25.83
|
57.69
|
3.617
|
19.015
|
22.505
|
1.717
|
40.247
|
340.000
|
56.01
|
128.00
|
18.84
|
96.76
|
10443.75
|
|
58
|
12
|
21
|
36
|
younger
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Alive s/p OHT
|
alive
|
80991
|
19210
|
23.72
|
99.00
|
50.61
|
3.50
|
12.33
|
87.67
|
14.74
|
4.81
|
1.05
|
85.41
|
11.13
|
2.41
|
8.48
|
0.52
|
1.56
|
80.00
|
8.27
|
3.76
|
7.41
|
13.68
|
0.00
|
30.83
|
77.74
|
10.83
|
14.14
|
19.85
|
2.81
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
59
|
13
|
0
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
411616
|
119210
|
28.96
|
90.82
|
73.63
|
5.88
|
74.30
|
25.70
|
2.80
|
6.46
|
57.94
|
19.20
|
18.72
|
4.13
|
19.10
|
15.42
|
15.30
|
53.51
|
13.78
|
25.19
|
0.21
|
47.23
|
53.77
|
5.75
|
5.92
|
22.63
|
3.46
|
2.55
|
0.74
|
74.33
|
630.00
|
1198.000
|
29.033
|
285.00
|
6.710
|
41.890
|
282.000
|
58.746
|
108.000
|
242.00
|
1363.000
|
17.372
|
74.166
|
4.089
|
381.000
|
11.334
|
76.55
|
183.00
|
145.00
|
703.00
|
339.000
|
938.00
|
642
|
424
|
191.00
|
439.00
|
51.380
|
409.000
|
169.000
|
31.768
|
254.000
|
3217.000
|
299.00
|
1084.00
|
546.00
|
877.00
|
6887.00
|
|
60
|
13
|
1
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
430509
|
79492
|
18.46
|
89.31
|
52.10
|
20.45
|
87.13
|
12.87
|
1.23
|
4.76
|
58.11
|
9.15
|
30.55
|
2.20
|
29.86
|
18.05
|
10.37
|
63.63
|
10.23
|
17.43
|
0.06
|
74.05
|
72.50
|
2.36
|
2.98
|
10.68
|
1.72
|
1.03
|
2.89
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
61
|
13
|
3
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
395713
|
119670
|
30.24
|
97.90
|
23.79
|
6.27
|
82.17
|
17.83
|
0.79
|
1.99
|
49.95
|
13.01
|
33.91
|
3.13
|
28.36
|
16.83
|
5.00
|
56.33
|
7.13
|
26.73
|
0.02
|
79.50
|
82.88
|
1.96
|
5.55
|
16.30
|
3.61
|
1.88
|
0.42
|
4.28
|
159.00
|
409.000
|
41.124
|
12.27
|
2.320
|
4.307
|
20.356
|
15.581
|
16.793
|
121.00
|
61.854
|
2.899
|
4.794
|
1.980
|
49.157
|
20.020
|
28.46
|
32.56
|
102.00
|
524.00
|
415.000
|
305.00
|
282
|
465
|
41.67
|
203.00
|
12.431
|
121.000
|
35.967
|
2.120
|
73.622
|
945.000
|
39.60
|
451.00
|
55.87
|
159.00
|
676.00
|
|
62
|
13
|
8
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
329840
|
73468
|
22.27
|
98.63
|
70.61
|
3.73
|
51.50
|
48.50
|
8.32
|
3.77
|
33.46
|
41.29
|
20.92
|
4.33
|
25.19
|
5.89
|
24.07
|
46.40
|
3.73
|
34.64
|
0.07
|
64.14
|
28.43
|
10.35
|
9.61
|
38.78
|
4.62
|
5.55
|
2.91
|
100.00
|
430.00
|
1063.000
|
57.719
|
245.00
|
135.000
|
21.182
|
84.771
|
41.826
|
160.000
|
229.00
|
580.000
|
22.030
|
72.412
|
10.670
|
229.000
|
25.806
|
54.08
|
223.00
|
143.00
|
966.00
|
310.000
|
777.00
|
1415
|
796
|
217.00
|
325.00
|
45.047
|
411.000
|
137.000
|
44.518
|
266.000
|
2406.000
|
193.00
|
908.00
|
321.00
|
732.00
|
7464.00
|
|
63
|
13
|
14
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
350294
|
88823
|
25.36
|
97.63
|
74.37
|
4.37
|
73.35
|
26.65
|
4.91
|
4.83
|
56.54
|
21.32
|
19.84
|
2.30
|
34.68
|
11.25
|
18.37
|
61.98
|
5.78
|
20.55
|
0.04
|
63.91
|
47.54
|
6.81
|
6.17
|
21.77
|
2.90
|
2.74
|
3.00
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
64
|
14
|
0
|
38
|
younger
|
Male
|
High
|
2
|
Yes
|
Yes
|
BTT
|
TAH
|
Died s/p OHT
|
dead
|
128451
|
60927
|
47.43
|
96.82
|
60.77
|
4.70
|
77.26
|
22.74
|
2.17
|
1.95
|
55.58
|
18.40
|
21.54
|
4.47
|
27.86
|
18.89
|
15.33
|
69.90
|
0.94
|
24.40
|
0.14
|
70.73
|
1.85
|
8.01
|
8.66
|
20.21
|
5.45
|
3.32
|
0.39
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
65
|
14
|
1
|
38
|
younger
|
Male
|
High
|
2
|
Yes
|
Yes
|
BTT
|
TAH
|
Died s/p OHT
|
dead
|
142796
|
22573
|
15.81
|
80.67
|
47.31
|
2.28
|
63.37
|
36.63
|
9.16
|
6.51
|
26.02
|
33.49
|
37.11
|
3.37
|
38.13
|
7.50
|
13.13
|
53.25
|
5.78
|
26.27
|
1.57
|
40.24
|
0.00
|
27.71
|
22.17
|
29.16
|
18.80
|
15.66
|
8.11
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
66
|
14
|
3
|
38
|
younger
|
Male
|
High
|
2
|
Yes
|
Yes
|
BTT
|
TAH
|
Died s/p OHT
|
dead
|
157167
|
38008
|
24.18
|
93.47
|
34.86
|
3.95
|
69.71
|
30.29
|
3.92
|
3.92
|
33.64
|
26.30
|
35.78
|
4.28
|
39.54
|
11.49
|
14.94
|
66.86
|
2.85
|
24.31
|
1.04
|
64.50
|
7.27
|
21.60
|
20.53
|
27.66
|
14.54
|
14.18
|
2.12
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
67
|
14
|
5
|
38
|
younger
|
Male
|
High
|
2
|
Yes
|
Yes
|
BTT
|
TAH
|
Died s/p OHT
|
dead
|
121823
|
23097
|
18.96
|
93.48
|
2.51
|
2.87
|
99.52
|
0.48
|
0.00
|
0.00
|
0.97
|
0.48
|
98.55
|
0.00
|
50.00
|
25.00
|
25.00
|
94.35
|
0.00
|
2.58
|
0.00
|
0.16
|
0.00
|
17.45
|
87.40
|
0.00
|
0.16
|
18.26
|
0.00
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
68
|
14
|
8
|
38
|
younger
|
Male
|
High
|
2
|
Yes
|
Yes
|
BTT
|
TAH
|
Died s/p OHT
|
dead
|
196515
|
88579
|
45.07
|
85.54
|
17.17
|
3.28
|
59.38
|
40.62
|
3.21
|
1.41
|
35.44
|
35.28
|
23.87
|
5.42
|
62.85
|
12.32
|
3.71
|
66.05
|
4.38
|
23.22
|
0.27
|
74.53
|
31.43
|
25.51
|
28.12
|
39.78
|
25.79
|
21.57
|
0.79
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
69
|
15
|
0
|
61
|
older
|
Male
|
High
|
1
|
Yes
|
No
|
BTT
|
TAH
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.18
|
33.63
|
188.000
|
37.494
|
2.31
|
5.509
|
0.910
|
1.590
|
29.263
|
1.605
|
50.08
|
10.790
|
1.920
|
1.782
|
1.980
|
112.000
|
13.845
|
6.25
|
8.93
|
62.53
|
69.30
|
213.000
|
403.00
|
1373
|
458
|
5.90
|
36.01
|
7.766
|
139.000
|
6.036
|
0.944
|
37.403
|
629.000
|
9.39
|
257.00
|
0.39
|
40.05
|
19.79
|
|
70
|
15
|
1
|
61
|
older
|
Male
|
High
|
1
|
Yes
|
No
|
BTT
|
TAH
|
Alive s/p OHT
|
alive
|
87827
|
15751
|
17.93
|
76.38
|
31.60
|
12.17
|
77.46
|
22.54
|
6.35
|
2.53
|
50.34
|
20.22
|
27.05
|
2.39
|
19.68
|
7.62
|
19.05
|
75.55
|
7.38
|
9.49
|
0.71
|
94.26
|
89.19
|
11.68
|
17.96
|
18.78
|
6.97
|
10.86
|
12.46
|
10.69
|
5.04
|
118.000
|
49.561
|
3.51
|
52.670
|
1.125
|
2.413
|
349.000
|
3.799
|
22.94
|
23.573
|
1.950
|
1.782
|
2.042
|
443.000
|
22.377
|
3.62
|
41.79
|
165.00
|
206.00
|
137.000
|
323.00
|
1573
|
1461
|
10.05
|
112.00
|
12.371
|
72.373
|
20.483
|
3.987
|
364.000
|
322.000
|
10.50
|
82.02
|
0.39
|
77.29
|
380.00
|
|
71
|
15
|
3
|
61
|
older
|
Male
|
High
|
1
|
Yes
|
No
|
BTT
|
TAH
|
Alive s/p OHT
|
alive
|
55539
|
13608
|
24.50
|
79.00
|
28.02
|
15.78
|
81.90
|
18.10
|
6.13
|
2.42
|
66.92
|
14.33
|
14.98
|
3.77
|
20.81
|
17.45
|
18.12
|
78.01
|
7.08
|
8.61
|
0.72
|
94.04
|
70.73
|
11.32
|
16.69
|
15.21
|
5.78
|
10.50
|
16.39
|
6.39
|
3.54
|
119.000
|
48.334
|
2.31
|
220.000
|
1.369
|
1.590
|
57.616
|
1.520
|
28.03
|
7.706
|
1.920
|
1.450
|
1.980
|
158.000
|
19.806
|
5.94
|
27.01
|
132.00
|
296.00
|
172.000
|
322.00
|
1138
|
1362
|
3.98
|
63.62
|
6.549
|
69.626
|
14.327
|
3.358
|
80.780
|
351.000
|
5.73
|
82.02
|
0.39
|
40.05
|
86.64
|
|
72
|
15
|
5
|
61
|
older
|
Male
|
High
|
1
|
Yes
|
No
|
BTT
|
TAH
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
5.33
|
34.44
|
200.000
|
52.572
|
3.51
|
474.000
|
2.603
|
1.773
|
79.861
|
1.605
|
55.35
|
7.706
|
1.920
|
1.494
|
2.382
|
177.000
|
14.690
|
7.33
|
45.46
|
166.00
|
572.00
|
205.000
|
488.00
|
1047
|
1280
|
10.05
|
102.00
|
4.485
|
119.000
|
19.099
|
7.153
|
161.000
|
644.000
|
9.39
|
221.00
|
0.39
|
83.29
|
162.00
|
|
73
|
15
|
8
|
61
|
older
|
Male
|
High
|
1
|
Yes
|
No
|
BTT
|
TAH
|
Alive s/p OHT
|
alive
|
295005
|
36785
|
12.47
|
94.58
|
36.55
|
9.14
|
86.32
|
13.68
|
2.45
|
1.82
|
37.01
|
12.52
|
49.18
|
1.29
|
16.24
|
4.71
|
12.47
|
8.36
|
1.73
|
6.64
|
0.81
|
84.78
|
46.55
|
12.52
|
9.28
|
5.94
|
1.73
|
4.47
|
2.49
|
6.39
|
108.00
|
240.000
|
53.791
|
2.31
|
408.000
|
2.900
|
1.686
|
53.531
|
2.648
|
69.91
|
77.150
|
3.312
|
4.794
|
1.980
|
165.000
|
11.334
|
12.51
|
30.71
|
150.00
|
348.00
|
270.000
|
546.00
|
1549
|
1547
|
23.97
|
71.06
|
2.980
|
140.000
|
20.483
|
4.619
|
182.000
|
735.000
|
18.04
|
283.00
|
0.39
|
83.29
|
86.64
|
|
74
|
15
|
14
|
61
|
older
|
Male
|
High
|
1
|
Yes
|
No
|
BTT
|
TAH
|
Alive s/p OHT
|
alive
|
60695
|
20471
|
33.73
|
94.91
|
6.93
|
7.42
|
83.97
|
16.03
|
3.40
|
0.69
|
24.36
|
14.71
|
59.33
|
1.60
|
18.43
|
5.07
|
11.52
|
54.82
|
2.91
|
16.10
|
0.50
|
82.44
|
30.00
|
51.08
|
13.25
|
10.41
|
3.68
|
11.31
|
6.06
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
75
|
16
|
0
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
52016
|
30920
|
59.44
|
95.77
|
55.57
|
24.62
|
58.73
|
41.27
|
2.14
|
0.97
|
53.01
|
5.05
|
5.45
|
36.50
|
9.39
|
81.29
|
2.88
|
81.48
|
5.65
|
9.02
|
0.48
|
97.75
|
71.83
|
0.67
|
22.89
|
0.34
|
1.78
|
0.49
|
NA
|
11.66
|
5.99
|
5.180
|
18.210
|
2.65
|
2.810
|
2.640
|
2.960
|
2.600
|
1.500
|
3.19
|
3.790
|
0.750
|
1.140
|
1.270
|
12.840
|
1.920
|
2.78
|
22.29
|
13.36
|
205.00
|
120.000
|
433.00
|
852
|
235
|
13.38
|
108.00
|
17.980
|
22.860
|
7.890
|
3.660
|
26.920
|
114.000
|
27.36
|
63.49
|
6.10
|
47.28
|
2960.00
|
|
76
|
16
|
1
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
75516
|
48801
|
64.62
|
92.13
|
52.90
|
18.64
|
70.39
|
29.61
|
2.92
|
2.21
|
62.18
|
5.13
|
7.98
|
24.70
|
13.79
|
69.54
|
3.19
|
84.14
|
3.64
|
6.36
|
0.41
|
94.93
|
43.78
|
0.57
|
15.61
|
0.23
|
0.91
|
0.36
|
NA
|
6.69
|
4.54
|
3.850
|
13.310
|
2.65
|
2.810
|
2.640
|
2.960
|
15.360
|
1.500
|
2.89
|
2.390
|
0.640
|
1.550
|
1.060
|
51.270
|
3.890
|
2.78
|
23.91
|
29.12
|
139.00
|
53.210
|
184.00
|
503
|
192
|
13.38
|
162.00
|
30.710
|
25.630
|
7.270
|
3.380
|
58.260
|
135.000
|
10.50
|
52.34
|
4.60
|
111.00
|
1306.00
|
|
77
|
16
|
3
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
250612
|
130454
|
52.05
|
95.75
|
60.05
|
3.29
|
46.04
|
53.96
|
1.34
|
3.89
|
33.08
|
32.47
|
8.29
|
26.15
|
3.56
|
34.61
|
0.09
|
76.71
|
0.39
|
13.20
|
0.28
|
39.79
|
14.38
|
2.26
|
1.56
|
10.45
|
2.89
|
3.04
|
0.08
|
22.58
|
5.49
|
6.580
|
15.690
|
2.77
|
2.810
|
2.640
|
2.960
|
19.820
|
1.500
|
3.96
|
4.710
|
1.750
|
5.060
|
1.270
|
242.000
|
7.410
|
6.23
|
43.70
|
45.47
|
205.00
|
139.000
|
201.00
|
614
|
498
|
27.61
|
259.00
|
37.910
|
41.540
|
13.790
|
4.520
|
90.330
|
187.000
|
20.67
|
91.18
|
20.95
|
94.90
|
1637.00
|
|
78
|
16
|
5
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
36.87
|
8.59
|
9.500
|
14.150
|
5.49
|
4.340
|
2.640
|
2.960
|
3.860
|
1.500
|
4.92
|
8.120
|
2.050
|
5.620
|
1.480
|
49.360
|
5.860
|
3.77
|
51.10
|
19.90
|
214.00
|
142.000
|
261.00
|
1163
|
556
|
27.24
|
145.00
|
37.770
|
41.540
|
20.750
|
5.700
|
54.980
|
210.000
|
28.80
|
99.93
|
38.44
|
100.00
|
3060.00
|
|
79
|
16
|
8
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
212478
|
120477
|
56.70
|
85.12
|
48.36
|
12.51
|
79.57
|
20.43
|
1.35
|
2.14
|
58.73
|
12.87
|
18.44
|
9.96
|
5.63
|
34.32
|
0.11
|
86.74
|
0.41
|
6.88
|
0.15
|
35.30
|
53.09
|
1.23
|
0.75
|
2.36
|
1.04
|
1.47
|
NA
|
39.75
|
7.52
|
10.260
|
14.990
|
6.70
|
4.900
|
2.640
|
3.060
|
5.960
|
1.500
|
5.57
|
11.870
|
2.670
|
6.610
|
1.480
|
49.000
|
5.860
|
3.86
|
55.20
|
17.87
|
278.00
|
105.000
|
428.00
|
1180
|
309
|
30.12
|
162.00
|
41.370
|
49.350
|
22.170
|
5.990
|
67.210
|
220.000
|
34.39
|
108.00
|
31.81
|
119.00
|
3873.00
|
|
80
|
17
|
0
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
86135
|
70306
|
81.62
|
93.71
|
74.50
|
1.59
|
61.74
|
38.26
|
1.91
|
2.67
|
33.97
|
14.50
|
26.81
|
24.71
|
20.67
|
62.47
|
9.03
|
73.00
|
2.00
|
24.71
|
1.27
|
20.23
|
71.43
|
0.48
|
12.79
|
0.38
|
3.24
|
0.48
|
4.60
|
2.81
|
7.26
|
7.290
|
173.000
|
2.65
|
2.810
|
2.640
|
2.960
|
12.550
|
1.500
|
4.28
|
8.850
|
0.640
|
0.770
|
1.160
|
2.930
|
3.890
|
3.28
|
27.19
|
22.83
|
145.00
|
70.170
|
765.00
|
1478
|
482
|
9.57
|
94.83
|
94.860
|
103.000
|
8.520
|
3.660
|
18.060
|
120.000
|
19.71
|
40.19
|
28.19
|
51.59
|
1276.00
|
|
81
|
17
|
1
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
81004
|
41334
|
51.03
|
94.06
|
53.64
|
2.99
|
61.70
|
38.30
|
3.10
|
2.75
|
39.07
|
13.34
|
22.12
|
25.47
|
12.58
|
63.11
|
15.99
|
78.92
|
2.93
|
17.38
|
3.35
|
23.24
|
71.88
|
0.52
|
11.70
|
0.09
|
1.29
|
0.52
|
9.83
|
7.64
|
4.08
|
4.170
|
41.980
|
2.65
|
2.810
|
2.640
|
2.960
|
133.000
|
1.500
|
2.63
|
7.760
|
0.420
|
1.770
|
0.910
|
98.210
|
8.740
|
4.54
|
15.89
|
77.14
|
211.00
|
64.900
|
374.00
|
1714
|
959
|
5.00
|
64.16
|
21.350
|
54.290
|
8.520
|
2.560
|
284.000
|
52.100
|
11.10
|
21.92
|
7.15
|
51.59
|
1301.00
|
|
82
|
17
|
3
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
0.22
|
3.20
|
3.220
|
21.580
|
2.65
|
2.810
|
2.640
|
2.960
|
15.210
|
1.500
|
2.63
|
2.520
|
0.420
|
0.020
|
0.680
|
58.680
|
7.800
|
3.77
|
8.37
|
31.94
|
83.16
|
74.380
|
388.00
|
644
|
432
|
2.90
|
49.55
|
8.660
|
30.220
|
4.630
|
1.790
|
14.940
|
45.600
|
11.69
|
14.26
|
1.53
|
29.96
|
690.00
|
|
83
|
17
|
5
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
90024
|
39434
|
43.80
|
95.32
|
68.83
|
5.62
|
71.59
|
28.41
|
0.85
|
1.66
|
35.23
|
13.02
|
35.80
|
15.96
|
22.94
|
52.37
|
5.85
|
76.42
|
1.14
|
21.83
|
0.62
|
14.54
|
48.57
|
0.38
|
4.69
|
0.00
|
0.80
|
0.38
|
0.62
|
0.58
|
3.63
|
3.220
|
25.010
|
2.65
|
2.810
|
2.640
|
2.960
|
23.480
|
1.500
|
2.63
|
2.390
|
0.420
|
0.100
|
0.860
|
31.820
|
7.670
|
11.61
|
15.89
|
41.91
|
454.00
|
91.330
|
506.00
|
670
|
421
|
3.21
|
54.44
|
14.370
|
37.430
|
6.660
|
2.300
|
33.500
|
58.260
|
15.08
|
19.42
|
6.10
|
41.52
|
1837.00
|
|
84
|
17
|
8
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
37.61
|
21.91
|
44.071
|
23.633
|
39.20
|
9.532
|
6.428
|
5.102
|
32.860
|
7.062
|
12.93
|
20.414
|
2.167
|
3.138
|
5.978
|
41.851
|
8.190
|
4.95
|
63.21
|
39.70
|
328.00
|
175.000
|
323.00
|
808
|
540
|
34.88
|
91.67
|
13.604
|
48.125
|
30.231
|
5.864
|
59.949
|
466.000
|
81.74
|
150.00
|
4.51
|
195.00
|
9741.00
|
|
85
|
17
|
14
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
106791
|
80532
|
75.41
|
88.82
|
79.92
|
1.57
|
62.32
|
37.68
|
1.70
|
4.02
|
28.04
|
22.86
|
34.11
|
15.00
|
23.92
|
39.41
|
15.03
|
61.61
|
3.66
|
34.55
|
1.97
|
20.36
|
73.33
|
0.80
|
8.57
|
0.27
|
1.79
|
0.98
|
0.71
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
86
|
18
|
0
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
60.38
|
32.81
|
186.000
|
38.833
|
32.98
|
21.709
|
3.051
|
5.621
|
37.885
|
11.091
|
29.33
|
189.000
|
6.338
|
16.504
|
4.089
|
278.000
|
17.668
|
7.17
|
59.99
|
54.99
|
695.00
|
232.000
|
211.00
|
828
|
1145
|
50.86
|
107.00
|
10.251
|
111.000
|
31.722
|
7.153
|
120.000
|
533.000
|
28.75
|
322.00
|
2.34
|
224.00
|
3524.00
|
|
87
|
18
|
1
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
63.95
|
40.14
|
249.000
|
28.453
|
32.98
|
21.709
|
3.357
|
4.711
|
28.562
|
11.091
|
38.39
|
220.000
|
6.089
|
13.032
|
4.089
|
124.000
|
15.114
|
5.01
|
59.99
|
41.74
|
638.00
|
289.000
|
303.00
|
509
|
713
|
59.63
|
121.00
|
9.867
|
123.000
|
56.352
|
7.153
|
82.196
|
685.000
|
46.72
|
384.00
|
14.39
|
229.00
|
5489.00
|
|
88
|
18
|
3
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
366713
|
77282
|
21.07
|
98.61
|
66.55
|
12.44
|
93.65
|
6.35
|
0.53
|
0.60
|
62.22
|
4.68
|
32.38
|
0.72
|
19.94
|
9.89
|
28.23
|
83.18
|
0.51
|
14.38
|
0.30
|
94.72
|
54.39
|
2.07
|
11.33
|
2.86
|
3.40
|
1.48
|
3.58
|
65.71
|
24.75
|
198.000
|
26.329
|
40.68
|
18.837
|
4.631
|
4.711
|
20.178
|
9.696
|
29.66
|
185.000
|
5.841
|
11.411
|
4.293
|
71.642
|
12.166
|
3.92
|
74.20
|
35.60
|
1110.00
|
222.000
|
300.00
|
489
|
627
|
50.86
|
126.00
|
9.479
|
104.000
|
51.467
|
10.275
|
77.933
|
727.000
|
62.42
|
422.00
|
11.03
|
219.00
|
9557.42
|
|
89
|
18
|
5
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
262251
|
55344
|
21.10
|
99.55
|
62.37
|
14.39
|
93.00
|
7.00
|
1.03
|
0.69
|
65.71
|
4.74
|
28.37
|
1.17
|
25.93
|
17.28
|
25.93
|
79.87
|
1.20
|
17.05
|
0.48
|
97.76
|
58.18
|
2.27
|
11.13
|
4.12
|
4.91
|
1.87
|
6.10
|
89.92
|
70.33
|
309.000
|
36.344
|
103.00
|
29.010
|
8.732
|
7.037
|
28.329
|
24.712
|
57.19
|
384.000
|
8.403
|
25.681
|
5.347
|
93.592
|
18.950
|
6.40
|
67.14
|
51.06
|
434.00
|
238.000
|
328.00
|
896
|
536
|
91.53
|
134.00
|
14.161
|
155.000
|
92.272
|
10.890
|
115.000
|
873.000
|
64.87
|
398.00
|
65.30
|
368.00
|
2247.00
|
|
90
|
18
|
8
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
309193
|
61091
|
19.76
|
98.15
|
56.62
|
7.24
|
83.61
|
16.39
|
3.27
|
1.52
|
58.03
|
11.90
|
28.13
|
1.93
|
24.65
|
11.36
|
1.52
|
79.53
|
1.45
|
13.33
|
0.97
|
92.63
|
25.76
|
3.50
|
9.23
|
7.57
|
3.73
|
3.48
|
0.00
|
60.38
|
17.64
|
183.000
|
34.233
|
42.23
|
20.268
|
6.645
|
4.488
|
29.029
|
11.795
|
27.05
|
177.000
|
6.089
|
10.605
|
3.887
|
154.000
|
12.585
|
3.77
|
56.39
|
56.34
|
1534.00
|
165.000
|
446.00
|
487
|
478
|
53.19
|
121.00
|
10.759
|
85.242
|
37.382
|
13.310
|
79.359
|
700.000
|
104.00
|
490.00
|
12.72
|
151.00
|
9557.42
|
|
91
|
19
|
0
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
147997
|
54649
|
36.93
|
91.75
|
79.63
|
7.62
|
74.86
|
25.14
|
0.60
|
3.45
|
62.45
|
22.87
|
12.30
|
2.38
|
45.98
|
9.28
|
13.61
|
74.33
|
2.67
|
21.19
|
0.84
|
91.31
|
90.15
|
3.58
|
2.25
|
21.79
|
1.36
|
0.58
|
0.34
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
92
|
19
|
1
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
177687
|
56400
|
31.74
|
97.13
|
69.76
|
12.60
|
81.83
|
18.17
|
0.65
|
1.61
|
55.77
|
16.13
|
26.02
|
2.09
|
53.19
|
10.70
|
11.57
|
82.92
|
1.38
|
13.98
|
0.28
|
89.83
|
85.59
|
3.78
|
1.85
|
14.65
|
1.30
|
0.68
|
0.18
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
93
|
19
|
3
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
233155
|
67140
|
28.80
|
98.44
|
74.89
|
5.32
|
81.96
|
18.04
|
1.00
|
1.88
|
49.03
|
16.59
|
32.90
|
1.48
|
49.92
|
7.20
|
12.83
|
83.44
|
1.39
|
12.98
|
0.14
|
89.70
|
69.70
|
4.58
|
1.62
|
13.89
|
1.08
|
1.08
|
0.17
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
94
|
19
|
5
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
99257
|
29455
|
29.68
|
97.33
|
62.48
|
4.73
|
80.60
|
19.40
|
1.84
|
1.25
|
45.06
|
18.36
|
35.25
|
1.33
|
44.61
|
6.32
|
12.27
|
82.89
|
0.88
|
13.20
|
0.09
|
83.55
|
35.29
|
7.23
|
3.54
|
14.75
|
2.29
|
3.02
|
1.20
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
95
|
19
|
8
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
76358
|
18702
|
24.49
|
97.32
|
29.48
|
11.10
|
86.29
|
13.71
|
2.43
|
0.50
|
37.92
|
12.13
|
48.32
|
1.63
|
46.45
|
11.70
|
11.35
|
83.96
|
0.20
|
10.50
|
0.06
|
57.82
|
50.00
|
9.95
|
29.60
|
10.20
|
0.64
|
9.36
|
0.80
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
96
|
20
|
0
|
37
|
younger
|
Female
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Died s/p OHT
|
dead
|
56374
|
12141
|
21.54
|
67.38
|
27.12
|
3.32
|
51.84
|
48.16
|
1.84
|
1.84
|
2.94
|
45.59
|
47.79
|
3.68
|
0.00
|
0.00
|
2.29
|
92.65
|
0.37
|
5.51
|
0.72
|
30.15
|
60.00
|
6.99
|
9.56
|
0.37
|
0.00
|
3.68
|
0.00
|
2.22
|
9.41
|
292.000
|
21.883
|
2.31
|
2.320
|
1.245
|
1.590
|
7.255
|
1.520
|
70.36
|
2.120
|
1.920
|
1.450
|
1.980
|
90.424
|
1.623
|
3.92
|
8.93
|
91.17
|
78.30
|
141.000
|
579.00
|
666
|
562
|
10.05
|
55.48
|
11.010
|
72.373
|
9.732
|
2.120
|
40.533
|
733.000
|
2.89
|
164.00
|
0.39
|
33.64
|
518.00
|
|
97
|
20
|
1
|
37
|
younger
|
Female
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Died s/p OHT
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
7.16
|
11.35
|
124.000
|
35.144
|
2.80
|
2.970
|
0.552
|
1.231
|
25.459
|
2.290
|
28.30
|
3.190
|
0.940
|
0.850
|
2.360
|
219.000
|
2.770
|
10.35
|
40.14
|
133.00
|
229.00
|
86.923
|
599.00
|
801
|
488
|
22.49
|
26.21
|
38.398
|
66.074
|
17.389
|
2.089
|
52.042
|
488.000
|
5.07
|
124.00
|
0.92
|
59.20
|
1144.00
|
|
98
|
20
|
3
|
37
|
younger
|
Female
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Died s/p OHT
|
dead
|
7018
|
3464
|
49.36
|
89.03
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
8.66
|
13.04
|
89.555
|
29.993
|
4.95
|
2.970
|
1.247
|
1.474
|
18.328
|
2.432
|
24.88
|
3.190
|
2.486
|
0.850
|
2.593
|
45.524
|
4.181
|
8.44
|
60.77
|
134.00
|
181.00
|
94.833
|
598.00
|
549
|
407
|
18.84
|
57.69
|
110.000
|
103.000
|
18.917
|
3.601
|
45.144
|
420.000
|
10.11
|
113.00
|
0.92
|
54.13
|
1900.00
|
|
99
|
20
|
5
|
37
|
younger
|
Female
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Died s/p OHT
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
10.69
|
16.87
|
180.000
|
45.496
|
4.61
|
2.320
|
1.825
|
1.686
|
21.569
|
2.111
|
48.20
|
2.120
|
1.920
|
1.450
|
2.382
|
148.000
|
2.224
|
14.68
|
41.79
|
117.00
|
278.00
|
137.000
|
520.00
|
821
|
424
|
13.47
|
107.00
|
34.287
|
84.751
|
13.657
|
6.521
|
58.902
|
618.000
|
8.24
|
146.00
|
0.39
|
65.11
|
1301.00
|
|
100
|
20
|
8
|
37
|
younger
|
Female
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Died s/p OHT
|
dead
|
80210
|
21828
|
27.21
|
85.74
|
20.38
|
3.50
|
61.98
|
38.02
|
8.55
|
2.90
|
5.50
|
34.96
|
56.18
|
3.36
|
0.00
|
0.00
|
3.21
|
89.31
|
0.61
|
2.60
|
1.49
|
23.66
|
0.00
|
21.37
|
9.16
|
1.07
|
0.15
|
5.34
|
1.31
|
132.00
|
18.42
|
230.000
|
38.833
|
20.88
|
7.954
|
3.203
|
8.499
|
16.494
|
4.406
|
56.12
|
2.120
|
26.710
|
15.890
|
5.564
|
79.472
|
18.095
|
17.57
|
118.00
|
101.00
|
250.00
|
163.000
|
563.00
|
804
|
352
|
72.23
|
160.00
|
30.736
|
184.000
|
43.034
|
13.310
|
76.501
|
902.000
|
49.14
|
176.00
|
58.43
|
277.00
|
6159.00
|
|
101
|
20
|
14
|
37
|
younger
|
Female
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Died s/p OHT
|
dead
|
55068
|
19145
|
34.77
|
75.16
|
16.90
|
3.30
|
64.00
|
36.00
|
4.21
|
2.11
|
10.53
|
34.11
|
52.42
|
2.95
|
0.00
|
0.00
|
2.94
|
92.42
|
0.00
|
3.37
|
1.00
|
22.74
|
0.00
|
12.42
|
11.16
|
0.63
|
0.00
|
4.63
|
0.00
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
102
|
20
|
21
|
37
|
younger
|
Female
|
High
|
2
|
Yes
|
NA
|
BTT
|
PVAD
|
Died s/p OHT
|
dead
|
76673
|
23615
|
30.80
|
83.39
|
26.27
|
2.06
|
63.55
|
36.45
|
5.42
|
5.42
|
5.17
|
34.48
|
58.13
|
2.22
|
0.00
|
0.00
|
2.72
|
89.41
|
0.00
|
3.45
|
1.95
|
18.23
|
4.55
|
18.23
|
8.87
|
1.23
|
0.25
|
4.68
|
0.00
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
103
|
21
|
0
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
226762
|
25868
|
11.41
|
89.41
|
25.31
|
12.05
|
55.06
|
44.94
|
11.31
|
2.69
|
14.79
|
42.57
|
39.95
|
2.69
|
19.87
|
3.88
|
32.88
|
68.95
|
10.48
|
13.57
|
0.59
|
80.62
|
22.67
|
31.91
|
19.38
|
15.58
|
10.37
|
16.55
|
8.18
|
2.18
|
2.45
|
60.794
|
34.617
|
2.31
|
2.320
|
1.245
|
1.590
|
68.832
|
1.520
|
7.45
|
2.120
|
1.920
|
1.450
|
1.980
|
130.000
|
3.544
|
14.61
|
5.57
|
47.27
|
442.00
|
52.814
|
249.00
|
328
|
245
|
3.98
|
55.48
|
2.230
|
19.937
|
2.737
|
3.987
|
21.566
|
118.000
|
2.89
|
23.91
|
0.39
|
52.69
|
1293.00
|
|
104
|
21
|
3
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
244049
|
39386
|
16.14
|
94.94
|
40.46
|
13.23
|
40.93
|
59.07
|
13.36
|
3.17
|
8.33
|
54.90
|
32.28
|
4.49
|
18.52
|
4.21
|
23.83
|
55.18
|
13.87
|
19.39
|
1.44
|
70.53
|
35.67
|
33.62
|
33.47
|
22.86
|
15.26
|
25.81
|
5.11
|
2.18
|
2.71
|
17.955
|
26.715
|
9.57
|
2.320
|
1.893
|
1.590
|
3.759
|
1.520
|
3.89
|
2.120
|
1.920
|
1.450
|
1.980
|
59.228
|
1.623
|
7.17
|
8.93
|
60.31
|
820.00
|
73.754
|
165.00
|
347
|
262
|
3.98
|
63.62
|
2.230
|
18.052
|
2.737
|
3.358
|
15.234
|
90.169
|
11.56
|
77.19
|
0.39
|
33.64
|
2726.00
|
|
105
|
21
|
5
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
344736
|
250121
|
72.55
|
89.83
|
7.54
|
3.81
|
45.84
|
54.16
|
3.96
|
1.53
|
13.39
|
52.70
|
32.18
|
1.72
|
67.14
|
2.92
|
2.44
|
74.68
|
6.81
|
13.91
|
0.21
|
63.33
|
58.02
|
33.05
|
30.94
|
19.83
|
13.96
|
26.74
|
0.22
|
2.18
|
4.12
|
15.278
|
24.202
|
12.27
|
2.320
|
2.457
|
1.590
|
4.492
|
1.520
|
3.07
|
2.120
|
1.920
|
1.450
|
2.558
|
43.554
|
1.623
|
2.86
|
23.32
|
48.13
|
1082.00
|
71.954
|
125.00
|
260
|
251
|
5.90
|
77.95
|
2.230
|
18.052
|
7.234
|
7.153
|
27.925
|
153.000
|
29.97
|
118.00
|
0.39
|
52.69
|
4714.00
|
|
106
|
21
|
8
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
216934
|
140093
|
64.58
|
96.37
|
4.78
|
7.22
|
22.49
|
77.51
|
13.49
|
0.64
|
7.22
|
67.61
|
15.17
|
10.00
|
35.88
|
7.82
|
17.22
|
51.28
|
39.40
|
6.56
|
1.33
|
62.59
|
58.06
|
66.67
|
64.59
|
53.00
|
50.26
|
63.78
|
4.97
|
2.18
|
2.45
|
24.797
|
23.042
|
8.26
|
2.320
|
1.625
|
1.590
|
3.060
|
1.520
|
4.03
|
2.120
|
1.920
|
1.450
|
1.980
|
44.329
|
0.827
|
2.13
|
5.57
|
54.35
|
370.00
|
84.966
|
89.52
|
348
|
255
|
3.98
|
46.41
|
2.230
|
9.990
|
2.520
|
2.120
|
9.035
|
55.097
|
2.89
|
23.91
|
0.39
|
27.20
|
1113.00
|
|
107
|
21
|
14
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
215820
|
66785
|
30.94
|
92.54
|
17.20
|
6.01
|
25.88
|
74.12
|
9.15
|
1.45
|
3.09
|
71.72
|
22.44
|
2.74
|
65.43
|
3.42
|
0.04
|
69.38
|
15.93
|
8.85
|
0.74
|
50.58
|
24.07
|
52.78
|
48.88
|
32.28
|
28.19
|
44.04
|
0.04
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
108
|
21
|
21
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
670692
|
523014
|
77.98
|
96.05
|
5.07
|
2.64
|
23.61
|
76.39
|
4.11
|
0.49
|
4.79
|
73.53
|
18.68
|
3.00
|
42.40
|
2.72
|
4.49
|
71.95
|
20.81
|
5.51
|
0.45
|
48.22
|
44.62
|
60.13
|
60.26
|
39.07
|
36.82
|
56.38
|
1.49
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
109
|
22
|
0
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
59.97
|
33.76
|
44.886
|
35.293
|
48.50
|
30.282
|
52.148
|
11.702
|
46.900
|
18.224
|
10.78
|
72.616
|
5.569
|
5.458
|
11.016
|
23.895
|
17.965
|
7.15
|
143.00
|
43.59
|
989.00
|
135.000
|
584.00
|
662
|
925
|
40.52
|
163.00
|
13.354
|
43.750
|
59.591
|
13.629
|
94.278
|
449.000
|
104.00
|
196.00
|
0.92
|
248.00
|
10443.75
|
|
110
|
22
|
1
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
6.42
|
13.61
|
24.600
|
30.292
|
4.17
|
3.956
|
10.044
|
3.491
|
27.540
|
3.964
|
5.69
|
3.190
|
0.940
|
1.805
|
2.360
|
69.905
|
14.225
|
3.22
|
64.43
|
56.18
|
263.00
|
47.791
|
495.00
|
624
|
1321
|
16.57
|
80.75
|
6.352
|
20.019
|
15.869
|
3.706
|
57.971
|
119.000
|
6.20
|
184.00
|
0.92
|
49.17
|
2297.00
|
|
111
|
22
|
3
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
121794
|
28129
|
23.10
|
97.05
|
52.79
|
16.42
|
70.08
|
29.92
|
0.29
|
0.54
|
50.71
|
21.46
|
22.96
|
4.86
|
34.18
|
12.51
|
23.49
|
39.78
|
0.31
|
55.18
|
0.35
|
96.01
|
16.67
|
1.74
|
5.27
|
23.38
|
2.95
|
1.00
|
0.10
|
2.77
|
13.90
|
16.630
|
13.668
|
9.16
|
2.970
|
3.372
|
2.078
|
10.277
|
6.347
|
2.66
|
4.260
|
1.165
|
1.090
|
2.360
|
36.910
|
7.340
|
2.59
|
60.77
|
27.10
|
698.00
|
100.000
|
514.00
|
323
|
606
|
25.83
|
54.27
|
4.276
|
23.867
|
33.318
|
4.024
|
31.548
|
264.000
|
61.66
|
124.00
|
0.92
|
69.60
|
10443.75
|
|
112
|
22
|
5
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
82157
|
17295
|
21.05
|
99.20
|
72.54
|
7.41
|
68.21
|
31.79
|
1.10
|
0.55
|
48.47
|
22.90
|
23.60
|
5.04
|
31.90
|
13.57
|
18.10
|
37.84
|
0.47
|
56.73
|
0.35
|
92.37
|
14.29
|
2.05
|
7.79
|
26.20
|
4.72
|
1.42
|
1.02
|
17.15
|
11.58
|
21.356
|
25.652
|
9.57
|
5.509
|
5.963
|
3.002
|
17.410
|
6.315
|
3.89
|
23.573
|
2.504
|
1.494
|
5.132
|
87.134
|
12.166
|
2.86
|
56.39
|
33.82
|
845.00
|
150.000
|
484.00
|
556
|
814
|
19.17
|
130.00
|
6.685
|
27.906
|
20.483
|
10.890
|
40.533
|
185.000
|
70.35
|
99.39
|
2.34
|
118.00
|
7899.00
|
|
113
|
22
|
8
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
154810
|
27067
|
17.48
|
98.04
|
72.18
|
5.03
|
74.91
|
25.09
|
0.90
|
1.65
|
58.05
|
14.76
|
20.82
|
6.37
|
23.43
|
25.71
|
22.57
|
44.94
|
0.60
|
49.36
|
0.71
|
86.59
|
22.73
|
3.30
|
7.64
|
20.15
|
4.87
|
1.72
|
0.00
|
10.19
|
18.30
|
30.677
|
25.655
|
16.47
|
9.100
|
6.693
|
4.896
|
29.159
|
3.022
|
8.07
|
3.688
|
1.295
|
1.557
|
8.079
|
68.056
|
13.360
|
5.90
|
58.29
|
31.79
|
671.00
|
47.791
|
696.00
|
506
|
704
|
18.84
|
102.00
|
12.748
|
17.993
|
11.877
|
5.864
|
44.162
|
198.000
|
49.71
|
115.00
|
1.39
|
114.00
|
10443.75
|
|
114
|
22
|
21
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
197936
|
15446
|
7.80
|
95.86
|
50.35
|
5.44
|
79.13
|
20.87
|
3.98
|
3.11
|
52.30
|
15.90
|
29.81
|
1.99
|
20.23
|
5.78
|
24.86
|
40.62
|
1.12
|
50.06
|
1.89
|
62.36
|
8.00
|
7.20
|
9.94
|
13.17
|
3.60
|
5.09
|
1.55
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
115
|
23
|
0
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
114586
|
35996
|
31.41
|
100.00
|
59.03
|
11.31
|
75.87
|
24.13
|
1.52
|
7.69
|
69.83
|
11.67
|
10.20
|
8.30
|
27.80
|
29.94
|
2.04
|
84.64
|
0.64
|
10.54
|
0.61
|
91.57
|
61.34
|
4.99
|
7.15
|
11.57
|
5.92
|
2.53
|
1.92
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
116
|
23
|
1
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
13.27
|
4.09
|
22.842
|
12.996
|
2.80
|
2.970
|
1.098
|
0.346
|
6.097
|
2.290
|
3.48
|
3.190
|
0.940
|
0.850
|
2.360
|
103.000
|
4.849
|
4.64
|
2.91
|
28.25
|
276.00
|
25.145
|
449.00
|
274
|
442
|
7.15
|
31.49
|
3.050
|
10.241
|
2.290
|
1.036
|
12.701
|
13.318
|
2.92
|
110.00
|
0.92
|
39.59
|
1621.00
|
|
117
|
23
|
3
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
349054
|
129419
|
37.08
|
95.25
|
62.35
|
8.81
|
79.64
|
20.36
|
2.72
|
4.45
|
57.70
|
13.93
|
25.89
|
2.48
|
57.14
|
13.49
|
8.69
|
74.29
|
3.76
|
12.35
|
1.41
|
85.87
|
62.53
|
3.41
|
5.43
|
7.67
|
1.40
|
1.92
|
1.06
|
72.99
|
13.61
|
25.476
|
15.689
|
9.16
|
4.312
|
3.544
|
2.168
|
12.912
|
2.432
|
6.98
|
3.190
|
1.430
|
0.850
|
5.769
|
30.560
|
4.849
|
4.20
|
59.53
|
23.70
|
268.00
|
56.470
|
507.00
|
350
|
496
|
15.38
|
108.00
|
3.050
|
23.400
|
13.860
|
7.400
|
61.926
|
158.000
|
3.85
|
123.00
|
0.92
|
108.00
|
2374.00
|
|
118
|
23
|
5
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
447309
|
176789
|
39.52
|
93.00
|
53.57
|
9.32
|
80.92
|
19.08
|
2.16
|
5.73
|
56.72
|
12.51
|
28.43
|
2.34
|
47.68
|
12.19
|
11.00
|
73.80
|
4.44
|
12.53
|
2.57
|
83.88
|
49.09
|
5.24
|
6.82
|
8.32
|
2.18
|
2.28
|
1.72
|
75.52
|
39.53
|
49.720
|
16.363
|
80.35
|
13.585
|
14.246
|
8.044
|
28.002
|
22.225
|
15.11
|
23.132
|
3.869
|
4.279
|
13.928
|
30.947
|
13.071
|
8.12
|
149.00
|
16.40
|
384.00
|
120.000
|
681.00
|
419
|
400
|
41.84
|
179.00
|
5.172
|
46.471
|
46.582
|
14.665
|
126.000
|
364.000
|
36.57
|
89.46
|
22.99
|
224.00
|
4716.00
|
|
119
|
23
|
8
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
558290
|
301468
|
54.00
|
95.62
|
47.73
|
7.04
|
79.13
|
20.87
|
3.43
|
7.49
|
53.19
|
13.48
|
30.32
|
3.01
|
36.36
|
14.54
|
12.10
|
70.55
|
5.15
|
11.89
|
3.51
|
79.79
|
52.89
|
6.98
|
8.50
|
7.82
|
1.92
|
3.09
|
1.96
|
55.93
|
9.15
|
32.389
|
17.862
|
2.80
|
2.970
|
3.716
|
1.989
|
10.711
|
2.290
|
7.10
|
NA
|
NA
|
12.466
|
2.360
|
17.125
|
3.019
|
4.20
|
57.05
|
18.04
|
905.00
|
265.000
|
855.00
|
393
|
618
|
22.99
|
71.07
|
3.050
|
22.929
|
32.290
|
4.131
|
50.068
|
170.000
|
NA
|
97.14
|
17.70
|
80.29
|
10443.75
|
|
120
|
23
|
14
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
479402
|
362845
|
75.69
|
97.95
|
44.30
|
6.30
|
76.53
|
23.47
|
3.90
|
11.10
|
34.99
|
12.04
|
50.31
|
2.66
|
21.93
|
14.88
|
10.05
|
70.21
|
4.66
|
7.31
|
7.73
|
49.96
|
29.38
|
13.44
|
17.37
|
5.64
|
3.01
|
6.95
|
2.28
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
121
|
23
|
21
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
334942
|
191418
|
57.15
|
93.28
|
70.35
|
4.04
|
70.89
|
29.11
|
6.20
|
17.70
|
56.45
|
19.57
|
20.44
|
3.53
|
38.91
|
14.64
|
10.35
|
55.11
|
6.61
|
14.34
|
8.70
|
80.96
|
58.92
|
7.58
|
7.96
|
10.11
|
2.08
|
2.08
|
2.48
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
122
|
24
|
0
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
129980
|
41600
|
32.00
|
91.40
|
39.03
|
2.85
|
49.63
|
50.37
|
7.02
|
11.55
|
28.37
|
29.11
|
20.79
|
21.72
|
19.43
|
28.34
|
9.80
|
69.59
|
4.90
|
20.33
|
6.13
|
31.15
|
33.60
|
20.15
|
22.27
|
0.28
|
4.25
|
16.54
|
2.09
|
33.99
|
28.59
|
16.040
|
25.430
|
12.01
|
7.920
|
6.660
|
3.530
|
19.960
|
3.020
|
7.39
|
10.910
|
3.870
|
5.620
|
7.350
|
29.050
|
11.190
|
6.77
|
80.92
|
26.34
|
485.00
|
156.000
|
684.00
|
574
|
531
|
28.71
|
233.00
|
9.210
|
37.430
|
28.250
|
15.220
|
112.000
|
158.000
|
41.01
|
59.87
|
50.92
|
139.00
|
4188.00
|
|
123
|
24
|
1
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
59478
|
20182
|
33.93
|
89.07
|
44.50
|
10.08
|
60.93
|
39.07
|
2.10
|
2.70
|
42.88
|
13.69
|
17.66
|
25.77
|
13.06
|
46.12
|
3.13
|
81.35
|
1.16
|
16.11
|
0.83
|
37.58
|
6.12
|
5.52
|
11.64
|
0.06
|
1.49
|
2.76
|
4.07
|
4.02
|
3.20
|
2.320
|
19.970
|
2.65
|
2.810
|
2.640
|
2.960
|
405.000
|
1.500
|
2.63
|
2.130
|
0.460
|
0.450
|
0.860
|
99.660
|
13.000
|
2.78
|
12.80
|
206.00
|
266.00
|
84.610
|
329.00
|
197
|
841
|
3.21
|
39.72
|
8.990
|
27.180
|
6.360
|
2.830
|
565.000
|
23.210
|
11.10
|
10.23
|
1.22
|
38.63
|
1588.00
|
|
124
|
24
|
3
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
6.69
|
9.68
|
5.520
|
72.480
|
2.65
|
2.810
|
2.640
|
2.960
|
12.830
|
1.500
|
3.81
|
3.490
|
3.700
|
1.140
|
1.870
|
30.080
|
8.600
|
3.57
|
26.36
|
120.00
|
339.00
|
166.000
|
633.00
|
484
|
808
|
12.17
|
90.21
|
79.330
|
79.230
|
12.440
|
4.520
|
64.780
|
75.100
|
48.83
|
38.04
|
18.56
|
55.88
|
3892.00
|
|
125
|
24
|
5
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
26646
|
9754
|
36.61
|
98.69
|
58.32
|
7.57
|
64.88
|
35.12
|
2.06
|
0.55
|
46.50
|
12.62
|
17.70
|
23.18
|
15.07
|
53.31
|
12.50
|
83.40
|
1.10
|
15.64
|
1.76
|
28.12
|
0.00
|
7.54
|
16.46
|
0.55
|
1.92
|
6.04
|
13.00
|
0.58
|
3.20
|
3.220
|
34.140
|
2.65
|
2.810
|
2.640
|
2.960
|
39.370
|
1.500
|
2.63
|
2.930
|
0.420
|
0.020
|
0.540
|
66.160
|
8.470
|
3.38
|
5.65
|
51.46
|
66.05
|
106.000
|
497.00
|
1069
|
1138
|
2.90
|
29.91
|
6.510
|
29.470
|
5.480
|
1.550
|
51.690
|
2.870
|
3.21
|
8.86
|
43.82
|
41.52
|
535.00
|
|
126
|
24
|
8
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
73641
|
21944
|
29.80
|
96.79
|
48.91
|
4.47
|
62.74
|
37.26
|
2.84
|
3.16
|
37.47
|
17.79
|
24.95
|
19.79
|
19.46
|
38.11
|
9.46
|
79.79
|
1.89
|
13.58
|
1.24
|
27.05
|
20.00
|
14.11
|
13.37
|
0.21
|
2.74
|
10.53
|
3.31
|
2.44
|
4.08
|
4.340
|
49.370
|
2.65
|
2.810
|
2.640
|
2.960
|
25.260
|
1.500
|
2.63
|
6.360
|
0.420
|
0.100
|
0.770
|
74.530
|
10.920
|
5.20
|
8.37
|
91.97
|
73.49
|
135.000
|
493.00
|
1207
|
1273
|
2.90
|
49.55
|
17.980
|
28.710
|
7.270
|
2.560
|
35.160
|
14.190
|
3.93
|
8.86
|
52.09
|
35.74
|
551.00
|
|
127
|
25
|
0
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
44004
|
20612
|
46.84
|
43.65
|
15.61
|
21.54
|
49.64
|
50.36
|
10.37
|
0.57
|
15.94
|
45.15
|
33.18
|
5.73
|
24.69
|
5.68
|
20.25
|
44.58
|
1.55
|
36.17
|
0.88
|
67.23
|
27.27
|
32.35
|
15.48
|
20.02
|
4.49
|
10.94
|
2.76
|
71.72
|
47.35
|
26.349
|
37.381
|
158.00
|
33.334
|
10.660
|
8.044
|
35.402
|
46.417
|
6.87
|
NA
|
10.690
|
8.792
|
2.398
|
99.342
|
9.045
|
6.48
|
97.08
|
46.45
|
297.00
|
181.000
|
191.00
|
567
|
222
|
49.74
|
80.75
|
6.059
|
37.993
|
25.593
|
20.817
|
174.000
|
399.000
|
44.78
|
92.77
|
41.52
|
NA
|
4980.00
|
|
128
|
25
|
1
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
99731
|
23424
|
23.49
|
91.81
|
41.33
|
10.89
|
35.24
|
64.76
|
11.83
|
1.62
|
18.71
|
59.21
|
16.19
|
5.89
|
28.16
|
5.89
|
18.53
|
39.98
|
4.66
|
42.16
|
2.33
|
84.07
|
2.63
|
22.55
|
15.21
|
40.28
|
6.07
|
9.70
|
3.96
|
29.43
|
17.11
|
20.632
|
25.281
|
48.50
|
22.252
|
5.900
|
4.692
|
222.000
|
11.168
|
3.91
|
72.616
|
4.422
|
2.060
|
2.360
|
191.000
|
11.918
|
7.37
|
68.02
|
138.00
|
324.00
|
136.000
|
149.00
|
347
|
223
|
30.17
|
80.75
|
4.036
|
30.061
|
12.370
|
7.619
|
280.000
|
331.000
|
21.43
|
70.73
|
2.56
|
274.00
|
3581.00
|
|
129
|
25
|
3
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
74.26
|
46.69
|
17.521
|
32.607
|
125.00
|
34.355
|
9.340
|
7.512
|
18.328
|
13.879
|
4.56
|
190.000
|
13.031
|
14.904
|
2.360
|
35.125
|
11.629
|
8.85
|
37.42
|
35.14
|
218.00
|
222.000
|
173.00
|
1649
|
290
|
71.92
|
85.48
|
3.557
|
52.272
|
44.557
|
15.485
|
186.000
|
514.000
|
30.19
|
168.00
|
48.69
|
743.00
|
1283.00
|
|
130
|
25
|
5
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
63.06
|
27.96
|
35.932
|
31.732
|
44.55
|
16.008
|
3.051
|
5.621
|
18.791
|
10.391
|
5.03
|
152.000
|
11.673
|
14.663
|
1.980
|
35.584
|
10.919
|
6.56
|
45.46
|
42.92
|
420.00
|
180.000
|
216.00
|
2058
|
291
|
95.02
|
121.00
|
5.314
|
63.342
|
43.034
|
23.444
|
164.000
|
411.000
|
28.75
|
257.00
|
28.67
|
330.00
|
1677.00
|
|
131
|
25
|
8
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
129637
|
50001
|
38.57
|
90.65
|
44.41
|
6.05
|
30.66
|
69.34
|
17.12
|
4.05
|
10.62
|
66.50
|
19.60
|
3.28
|
26.41
|
3.46
|
13.37
|
39.74
|
7.15
|
39.49
|
11.91
|
71.97
|
2.70
|
32.48
|
33.14
|
43.91
|
7.81
|
22.70
|
3.82
|
40.52
|
23.75
|
22.842
|
21.835
|
74.36
|
13.123
|
5.021
|
5.931
|
27.077
|
9.272
|
4.67
|
134.000
|
12.603
|
9.710
|
2.360
|
53.765
|
8.759
|
6.48
|
60.77
|
35.38
|
426.00
|
223.000
|
224.00
|
1195
|
211
|
57.75
|
93.20
|
3.857
|
32.945
|
29.201
|
15.485
|
185.000
|
523.000
|
35.17
|
121.00
|
27.51
|
602.00
|
4088.00
|
|
132
|
25
|
14
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
134368
|
46363
|
34.50
|
88.32
|
34.25
|
4.63
|
32.47
|
67.53
|
12.86
|
8.38
|
8.91
|
64.89
|
22.88
|
3.32
|
24.78
|
3.89
|
13.58
|
36.16
|
6.01
|
40.33
|
13.77
|
72.11
|
1.89
|
29.89
|
26.36
|
42.80
|
6.17
|
15.97
|
3.29
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
133
|
26
|
0
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
280.00
|
525.00
|
894.000
|
111.000
|
286.00
|
113.000
|
13.198
|
30.593
|
9.360
|
23.275
|
174.00
|
653.000
|
95.232
|
163.000
|
1.980
|
163.000
|
80.128
|
16.37
|
766.00
|
93.58
|
660.00
|
253.000
|
702.00
|
389
|
553
|
193.00
|
427.00
|
39.685
|
321.000
|
496.000
|
9.657
|
37.403
|
1784.000
|
157.00
|
665.00
|
295.00
|
2180.00
|
6363.00
|
|
134
|
26
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
209.00
|
380.00
|
836.000
|
74.333
|
164.00
|
60.004
|
8.996
|
20.619
|
71.052
|
23.994
|
124.00
|
456.000
|
58.649
|
117.000
|
1.980
|
209.000
|
71.003
|
17.17
|
459.00
|
118.00
|
531.00
|
192.000
|
679.00
|
459
|
769
|
168.00
|
407.00
|
51.595
|
250.000
|
307.000
|
8.411
|
70.721
|
1328.000
|
113.00
|
567.00
|
236.00
|
1572.00
|
3605.00
|
|
135
|
26
|
3
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
288.00
|
641.00
|
1098.000
|
122.000
|
139.00
|
111.000
|
14.282
|
48.154
|
80.298
|
48.404
|
230.00
|
657.000
|
85.433
|
180.000
|
1.980
|
217.000
|
124.000
|
41.99
|
680.00
|
136.00
|
787.00
|
257.000
|
715.00
|
735
|
832
|
188.00
|
584.00
|
73.519
|
363.000
|
526.000
|
11.500
|
67.799
|
1782.000
|
135.00
|
732.00
|
335.00
|
3573.00
|
6153.00
|
|
136
|
27
|
0
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
59881
|
8971
|
14.98
|
97.40
|
50.06
|
9.57
|
75.84
|
24.16
|
3.59
|
8.13
|
64.83
|
13.40
|
15.67
|
6.10
|
19.31
|
18.81
|
16.83
|
76.67
|
6.10
|
6.82
|
2.00
|
88.16
|
47.06
|
10.53
|
11.24
|
10.41
|
9.93
|
6.94
|
11.93
|
16.38
|
67.35
|
180.000
|
62.930
|
127.00
|
2.970
|
36.468
|
4.794
|
25.690
|
5.645
|
46.84
|
139.000
|
1.295
|
17.624
|
2.360
|
148.000
|
9.904
|
9.46
|
143.00
|
64.82
|
261.00
|
507.000
|
809.00
|
891
|
476
|
76.81
|
136.00
|
18.740
|
113.000
|
98.896
|
20.817
|
358.000
|
925.000
|
91.47
|
168.00
|
123.00
|
653.00
|
761.00
|
|
137
|
27
|
1
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
60193
|
23874
|
39.66
|
96.72
|
58.83
|
16.24
|
80.37
|
19.63
|
1.87
|
3.31
|
68.58
|
10.62
|
14.72
|
6.08
|
21.33
|
22.83
|
3.80
|
80.61
|
3.63
|
8.03
|
0.33
|
94.16
|
57.26
|
2.96
|
5.01
|
10.19
|
4.72
|
1.41
|
1.24
|
3.57
|
32.49
|
145.000
|
59.668
|
35.43
|
2.970
|
12.151
|
4.287
|
29.390
|
5.645
|
54.69
|
26.852
|
0.940
|
6.660
|
2.360
|
142.000
|
12.494
|
6.13
|
91.76
|
118.00
|
294.00
|
354.000
|
659.00
|
811
|
759
|
35.98
|
83.92
|
12.544
|
90.832
|
44.557
|
39.375
|
223.000
|
830.000
|
67.87
|
147.00
|
58.83
|
260.00
|
528.00
|
|
138
|
27
|
3
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
8.66
|
39.21
|
135.000
|
67.453
|
61.20
|
3.273
|
20.290
|
5.826
|
23.842
|
5.819
|
45.26
|
64.203
|
1.569
|
9.863
|
3.090
|
66.315
|
10.766
|
5.31
|
155.00
|
68.00
|
355.00
|
420.000
|
945.00
|
1063
|
603
|
56.22
|
136.00
|
16.032
|
94.647
|
49.105
|
19.468
|
230.000
|
804.000
|
72.71
|
206.00
|
83.10
|
435.00
|
814.00
|
|
139
|
27
|
5
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
135335
|
27834
|
20.57
|
97.35
|
44.75
|
5.61
|
63.22
|
36.78
|
8.49
|
17.63
|
39.01
|
23.16
|
30.72
|
7.11
|
17.53
|
10.38
|
12.16
|
42.70
|
4.93
|
19.28
|
4.68
|
67.37
|
4.10
|
26.64
|
17.76
|
19.21
|
14.54
|
14.47
|
10.60
|
2.18
|
38.51
|
215.000
|
76.341
|
16.50
|
2.320
|
10.862
|
2.224
|
8.932
|
1.520
|
53.96
|
36.506
|
1.920
|
7.437
|
1.980
|
105.000
|
8.874
|
7.63
|
77.70
|
68.16
|
389.00
|
204.000
|
723.00
|
1321
|
487
|
50.26
|
160.00
|
12.674
|
125.000
|
27.485
|
45.764
|
177.000
|
710.000
|
55.82
|
360.00
|
90.56
|
239.00
|
351.00
|
|
140
|
27
|
8
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
247113
|
62042
|
25.11
|
96.41
|
43.93
|
14.82
|
78.42
|
21.58
|
2.52
|
5.24
|
65.35
|
12.22
|
17.53
|
4.90
|
20.39
|
17.20
|
2.93
|
77.86
|
2.11
|
8.78
|
2.94
|
84.64
|
9.70
|
10.64
|
13.93
|
9.90
|
11.59
|
8.48
|
3.51
|
22.57
|
29.97
|
91.301
|
58.481
|
24.03
|
7.004
|
8.899
|
5.515
|
27.077
|
7.972
|
25.93
|
37.523
|
2.650
|
3.138
|
5.142
|
70.641
|
12.783
|
7.59
|
94.97
|
56.14
|
551.00
|
288.000
|
717.00
|
1003
|
373
|
39.51
|
130.00
|
9.800
|
75.273
|
27.655
|
7.838
|
145.000
|
524.000
|
52.18
|
137.00
|
41.89
|
251.00
|
338.00
|
|
141
|
27
|
14
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
142053
|
56983
|
40.11
|
93.01
|
62.72
|
12.87
|
85.10
|
14.90
|
0.69
|
1.64
|
68.02
|
8.40
|
21.03
|
2.55
|
17.03
|
13.68
|
1.08
|
83.33
|
0.37
|
7.38
|
0.26
|
79.00
|
16.07
|
3.01
|
5.85
|
5.16
|
5.24
|
1.77
|
0.70
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
142
|
27
|
21
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
60384
|
17029
|
28.20
|
93.65
|
40.96
|
21.36
|
83.80
|
16.20
|
1.47
|
2.32
|
63.52
|
9.69
|
23.80
|
2.99
|
23.55
|
15.58
|
0.91
|
78.16
|
1.09
|
8.13
|
1.00
|
83.65
|
17.72
|
4.70
|
8.42
|
5.99
|
7.22
|
3.76
|
1.56
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
143
|
28
|
0
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
47645
|
37377
|
78.45
|
94.61
|
2.47
|
9.41
|
78.56
|
17.02
|
1.71
|
4.63
|
34.94
|
16.09
|
44.77
|
4.21
|
4.35
|
28.99
|
59.42
|
67.26
|
3.10
|
26.22
|
7.50
|
72.82
|
53.90
|
13.50
|
28.20
|
3.04
|
7.70
|
7.67
|
0.96
|
9.61
|
35.14
|
22.350
|
20.460
|
3.79
|
22.660
|
3.480
|
3.530
|
9.020
|
3.210
|
9.75
|
7.060
|
1.210
|
1.550
|
0.770
|
20.320
|
2.980
|
2.78
|
20.67
|
13.18
|
290.00
|
37.840
|
754.00
|
653
|
382
|
20.62
|
170.00
|
9.210
|
42.210
|
9.160
|
3.660
|
54.980
|
131.000
|
33.28
|
78.76
|
74.92
|
178.00
|
1807.00
|
|
144
|
28
|
1
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
87452
|
43025
|
49.20
|
91.54
|
1.98
|
26.71
|
92.06
|
6.76
|
0.77
|
4.67
|
47.21
|
4.70
|
45.17
|
2.92
|
6.06
|
18.18
|
60.61
|
81.01
|
3.95
|
14.10
|
12.04
|
85.97
|
79.84
|
5.82
|
14.15
|
1.11
|
4.77
|
2.84
|
0.00
|
12.71
|
13.67
|
11.390
|
16.950
|
2.77
|
3.020
|
2.690
|
2.960
|
29.730
|
1.790
|
5.89
|
3.490
|
1.610
|
2.220
|
2.640
|
103.000
|
6.370
|
5.67
|
25.55
|
37.67
|
369.00
|
23.230
|
830.00
|
341
|
373
|
18.73
|
111.00
|
6.740
|
23.260
|
13.120
|
5.700
|
76.070
|
128.000
|
32.15
|
77.14
|
17.37
|
97.61
|
3031.00
|
|
145
|
28
|
3
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
124056
|
80873
|
65.19
|
95.41
|
0.37
|
14.36
|
90.47
|
5.98
|
0.39
|
0.60
|
41.84
|
4.26
|
51.08
|
2.81
|
18.82
|
35.29
|
43.53
|
86.97
|
0.10
|
11.32
|
1.10
|
75.49
|
12.12
|
3.80
|
14.94
|
0.24
|
0.88
|
3.25
|
0.00
|
37.44
|
26.01
|
28.120
|
25.360
|
9.28
|
13.970
|
5.470
|
5.150
|
38.770
|
3.780
|
11.03
|
16.650
|
3.350
|
5.760
|
5.140
|
32.780
|
7.800
|
5.95
|
60.09
|
24.86
|
338.00
|
39.760
|
682.00
|
2208
|
349
|
27.24
|
174.00
|
11.700
|
30.960
|
38.010
|
10.120
|
76.070
|
213.000
|
35.85
|
86.63
|
43.82
|
185.00
|
2088.00
|
|
146
|
28
|
5
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
27.69
|
16.65
|
26.730
|
24.380
|
9.95
|
7.920
|
2.820
|
4.000
|
58.110
|
1.790
|
8.23
|
7.760
|
1.750
|
3.200
|
3.390
|
44.050
|
4.860
|
3.77
|
51.92
|
25.90
|
506.00
|
38.490
|
451.00
|
1325
|
306
|
19.69
|
141.00
|
6.280
|
18.330
|
27.170
|
7.470
|
68.020
|
184.000
|
35.13
|
89.68
|
25.77
|
160.00
|
2691.00
|
|
147
|
28
|
8
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
87840
|
57990
|
66.02
|
95.60
|
0.11
|
15.80
|
89.00
|
9.49
|
1.56
|
5.02
|
45.89
|
7.18
|
43.54
|
3.39
|
6.10
|
19.51
|
56.71
|
84.23
|
3.14
|
9.95
|
10.70
|
72.13
|
52.73
|
7.06
|
17.53
|
0.74
|
3.13
|
5.13
|
0.13
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
148
|
29
|
0
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
28.88
|
11.37
|
38.621
|
18.644
|
2.81
|
9.882
|
3.700
|
4.484
|
10.147
|
2.017
|
4.37
|
16.099
|
1.929
|
2.217
|
2.373
|
22.448
|
5.366
|
2.67
|
56.84
|
19.09
|
121.00
|
165.000
|
278.00
|
2642
|
893
|
12.58
|
118.00
|
9.063
|
71.540
|
36.892
|
9.225
|
98.841
|
153.000
|
9.87
|
43.25
|
1.92
|
91.04
|
849.00
|
|
149
|
29
|
1
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
32.32
|
11.74
|
23.505
|
14.109
|
2.81
|
8.767
|
2.565
|
4.264
|
25.300
|
2.483
|
4.11
|
23.064
|
1.929
|
2.217
|
2.373
|
38.737
|
17.441
|
3.48
|
47.53
|
43.53
|
86.72
|
138.000
|
254.00
|
4090
|
934
|
12.58
|
156.00
|
9.063
|
38.049
|
32.360
|
7.475
|
132.000
|
173.000
|
9.37
|
53.25
|
1.92
|
97.03
|
1026.00
|
|
150
|
29
|
3
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
25.42
|
7.75
|
19.917
|
18.280
|
2.72
|
2.813
|
1.666
|
2.416
|
11.420
|
1.760
|
3.33
|
3.635
|
1.630
|
1.230
|
1.693
|
23.780
|
15.880
|
17.02
|
38.18
|
30.19
|
301.00
|
163.000
|
98.45
|
422
|
469
|
7.34
|
128.00
|
14.917
|
59.891
|
23.381
|
4.874
|
81.038
|
142.000
|
5.65
|
29.22
|
1.92
|
55.92
|
693.00
|
|
151
|
29
|
5
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
16.71
|
7.06
|
19.917
|
15.917
|
2.72
|
2.813
|
1.666
|
2.229
|
11.848
|
12.332
|
3.08
|
1.100
|
1.350
|
1.230
|
2.025
|
15.579
|
10.484
|
3.34
|
28.88
|
28.36
|
209.00
|
154.000
|
88.98
|
365
|
499
|
7.34
|
107.00
|
36.898
|
76.513
|
22.115
|
5.734
|
83.628
|
132.000
|
5.65
|
26.13
|
1.92
|
61.63
|
668.00
|
|
152
|
29
|
8
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
40.77
|
11.37
|
32.156
|
21.931
|
2.81
|
11.022
|
2.105
|
3.003
|
6.884
|
14.236
|
3.08
|
1.969
|
2.247
|
3.356
|
2.828
|
13.545
|
9.354
|
3.89
|
55.29
|
20.23
|
152.00
|
235.000
|
81.91
|
458
|
893
|
17.01
|
128.00
|
38.439
|
108.000
|
32.360
|
9.225
|
88.759
|
142.000
|
7.31
|
140.00
|
1.92
|
79.15
|
1019.00
|
|
153
|
30
|
0
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
196315
|
122053
|
62.17
|
95.37
|
60.66
|
22.66
|
91.10
|
8.90
|
0.45
|
4.95
|
85.57
|
6.30
|
5.42
|
2.71
|
13.13
|
28.32
|
20.05
|
86.32
|
2.45
|
5.42
|
0.96
|
98.68
|
94.19
|
2.50
|
2.96
|
4.63
|
1.24
|
0.33
|
0.89
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
154
|
30
|
1
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
91909
|
37622
|
40.93
|
96.10
|
43.22
|
38.85
|
95.48
|
4.52
|
0.14
|
2.19
|
85.07
|
3.09
|
10.33
|
1.51
|
21.03
|
27.23
|
9.08
|
88.84
|
1.13
|
3.91
|
0.23
|
98.77
|
88.27
|
1.16
|
1.16
|
2.34
|
0.38
|
0.21
|
0.45
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
155
|
30
|
5
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
153762
|
86281
|
56.11
|
95.83
|
65.54
|
24.24
|
91.12
|
8.88
|
0.65
|
2.84
|
81.43
|
6.30
|
9.62
|
2.65
|
21.14
|
28.73
|
15.89
|
83.00
|
1.97
|
6.74
|
0.36
|
98.22
|
93.16
|
3.20
|
2.29
|
5.47
|
1.11
|
0.77
|
1.56
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
156
|
30
|
8
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
82406
|
47485
|
57.62
|
92.71
|
65.75
|
17.46
|
91.79
|
8.21
|
1.08
|
1.99
|
76.35
|
5.70
|
15.41
|
2.54
|
20.56
|
26.95
|
11.99
|
78.67
|
1.35
|
7.97
|
0.28
|
97.55
|
85.62
|
2.65
|
2.56
|
5.19
|
1.18
|
0.86
|
1.79
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
157
|
31
|
0
|
62
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
CMAG
|
Died
|
dead
|
105070
|
38521
|
36.66
|
98.39
|
47.84
|
12.88
|
60.82
|
39.18
|
11.08
|
2.89
|
33.87
|
29.43
|
26.68
|
10.01
|
19.72
|
21.92
|
2.99
|
59.35
|
14.64
|
20.23
|
3.45
|
81.40
|
46.10
|
24.47
|
24.27
|
24.04
|
11.00
|
17.02
|
1.51
|
8.54
|
60.57
|
363.000
|
41.314
|
133.00
|
3.271
|
11.758
|
2.224
|
42.519
|
17.512
|
99.72
|
208.000
|
1.950
|
1.450
|
2.382
|
76.080
|
15.538
|
10.70
|
41.79
|
124.00
|
480.00
|
240.000
|
583.00
|
1205
|
2213
|
43.09
|
202.00
|
15.656
|
88.621
|
44.444
|
4.619
|
159.000
|
99.662
|
8.24
|
246.00
|
0.39
|
1505.00
|
1842.00
|
|
158
|
31
|
1
|
62
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
CMAG
|
Died
|
dead
|
162336
|
73080
|
45.02
|
99.08
|
49.35
|
13.95
|
74.24
|
25.76
|
7.77
|
3.84
|
45.21
|
19.33
|
28.85
|
6.61
|
19.16
|
23.45
|
0.81
|
65.34
|
14.40
|
13.50
|
3.17
|
86.68
|
71.39
|
23.26
|
17.70
|
15.21
|
6.93
|
13.29
|
0.88
|
17.15
|
153.00
|
437.000
|
50.974
|
202.00
|
14.614
|
15.550
|
2.802
|
77.669
|
27.576
|
127.00
|
346.000
|
2.504
|
2.393
|
2.558
|
388.000
|
22.806
|
20.90
|
59.99
|
150.00
|
433.00
|
182.000
|
771.00
|
1505
|
2390
|
54.32
|
262.00
|
13.926
|
102.000
|
68.086
|
7.153
|
313.000
|
169.000
|
9.39
|
373.00
|
0.39
|
2102.00
|
1842.00
|
|
159
|
31
|
3
|
62
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
CMAG
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
21.40
|
85.72
|
386.000
|
52.666
|
141.00
|
13.237
|
8.380
|
3.206
|
207.000
|
23.994
|
108.00
|
256.000
|
3.104
|
4.254
|
3.299
|
844.000
|
34.327
|
22.27
|
70.68
|
205.00
|
540.00
|
278.000
|
454.00
|
1971
|
4894
|
52.04
|
226.00
|
9.089
|
95.130
|
74.895
|
9.657
|
206.000
|
230.000
|
12.58
|
318.00
|
19.30
|
1420.00
|
1900.00
|
|
160
|
31
|
5
|
62
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
CMAG
|
Died
|
dead
|
111264
|
60487
|
54.36
|
99.15
|
45.94
|
16.13
|
73.16
|
26.84
|
4.64
|
1.82
|
52.04
|
20.07
|
20.94
|
6.95
|
22.93
|
23.47
|
0.50
|
77.11
|
9.30
|
9.58
|
1.20
|
88.92
|
81.82
|
22.20
|
19.01
|
16.98
|
6.89
|
13.57
|
1.36
|
6.39
|
80.87
|
363.000
|
54.635
|
155.00
|
11.208
|
6.816
|
1.861
|
71.052
|
30.424
|
100.00
|
233.000
|
1.920
|
1.450
|
2.382
|
42.999
|
22.377
|
14.33
|
34.41
|
116.00
|
363.00
|
200.000
|
364.00
|
1276
|
1849
|
38.67
|
198.00
|
7.295
|
92.379
|
47.259
|
9.966
|
195.000
|
136.000
|
2.89
|
326.00
|
19.30
|
1292.00
|
1072.00
|
|
161
|
31
|
8
|
62
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
CMAG
|
Died
|
dead
|
165870
|
67935
|
40.96
|
98.88
|
43.01
|
15.86
|
70.23
|
29.77
|
7.85
|
1.88
|
40.39
|
20.49
|
29.66
|
9.46
|
22.70
|
29.00
|
1.88
|
68.61
|
9.60
|
16.11
|
1.47
|
89.57
|
69.00
|
20.12
|
16.59
|
17.10
|
7.02
|
12.40
|
2.06
|
8.54
|
55.67
|
299.000
|
54.916
|
168.00
|
10.543
|
4.146
|
2.224
|
51.707
|
60.924
|
80.71
|
211.000
|
2.314
|
2.393
|
3.299
|
24.277
|
15.538
|
9.59
|
56.39
|
99.84
|
439.00
|
210.000
|
312.00
|
968
|
1056
|
55.43
|
187.00
|
9.350
|
73.454
|
31.722
|
12.711
|
151.000
|
109.000
|
5.73
|
251.00
|
0.39
|
1247.00
|
1465.00
|
|
162
|
32
|
0
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.77
|
2.98
|
33.241
|
14.342
|
3.42
|
2.970
|
0.420
|
0.853
|
3.452
|
2.290
|
13.82
|
3.190
|
0.940
|
0.850
|
2.360
|
50.340
|
2.770
|
5.07
|
17.40
|
64.31
|
653.00
|
26.894
|
241.00
|
316
|
496
|
12.21
|
15.96
|
3.050
|
12.001
|
8.034
|
2.282
|
17.749
|
119.000
|
3.85
|
236.00
|
0.92
|
44.32
|
1793.00
|
|
163
|
32
|
1
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.77
|
24.67
|
100.000
|
10.761
|
20.24
|
2.970
|
8.281
|
0.853
|
136.000
|
3.964
|
19.25
|
3.688
|
0.940
|
1.090
|
2.360
|
130.000
|
11.629
|
6.13
|
45.48
|
139.00
|
541.00
|
88.075
|
437.00
|
264
|
768
|
20.97
|
77.56
|
5.231
|
40.568
|
66.481
|
1.717
|
408.000
|
245.000
|
55.44
|
141.00
|
16.95
|
74.91
|
10443.75
|
|
164
|
32
|
3
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
424375
|
21971
|
5.18
|
71.78
|
56.98
|
18.53
|
79.84
|
20.16
|
3.08
|
0.51
|
57.91
|
14.07
|
24.06
|
3.97
|
31.74
|
15.95
|
13.65
|
57.94
|
0.65
|
28.51
|
0.17
|
93.57
|
53.33
|
2.87
|
9.69
|
15.61
|
3.87
|
2.16
|
3.99
|
2.18
|
6.65
|
125.000
|
14.770
|
4.61
|
2.320
|
14.644
|
1.590
|
22.499
|
1.520
|
18.98
|
2.120
|
1.920
|
1.450
|
1.980
|
72.656
|
11.749
|
1.99
|
23.32
|
48.13
|
195.00
|
286.000
|
323.00
|
579
|
874
|
10.05
|
63.62
|
2.448
|
45.479
|
16.356
|
0.944
|
52.863
|
60.307
|
4.34
|
61.08
|
0.39
|
52.69
|
813.00
|
|
165
|
32
|
5
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.77
|
10.24
|
45.698
|
19.436
|
2.80
|
2.970
|
13.723
|
0.458
|
31.010
|
2.290
|
9.91
|
3.190
|
0.940
|
0.850
|
2.360
|
109.000
|
9.904
|
3.42
|
31.87
|
57.12
|
177.00
|
35.314
|
337.00
|
361
|
834
|
30.99
|
61.08
|
3.916
|
39.476
|
11.877
|
2.878
|
32.504
|
142.000
|
2.92
|
154.00
|
0.92
|
69.60
|
1160.00
|
|
166
|
32
|
8
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
177249
|
103442
|
58.36
|
76.40
|
15.43
|
5.64
|
65.13
|
34.87
|
5.21
|
0.20
|
35.56
|
27.91
|
31.90
|
4.62
|
26.60
|
8.28
|
13.24
|
67.89
|
0.29
|
20.13
|
0.00
|
78.60
|
22.22
|
5.50
|
16.09
|
25.56
|
6.80
|
4.73
|
0.89
|
2.18
|
2.98
|
74.914
|
45.496
|
2.31
|
2.320
|
4.146
|
1.590
|
63.920
|
1.520
|
21.62
|
2.120
|
20.861
|
1.450
|
1.980
|
773.000
|
19.378
|
33.60
|
45.46
|
368.00
|
630.00
|
220.000
|
265.00
|
523
|
1437
|
110.00
|
102.00
|
4.899
|
63.342
|
35.967
|
3.045
|
101.000
|
80.459
|
15.43
|
193.00
|
0.39
|
234.00
|
2752.00
|
Identification of clusters
We double standardized the data and biclustered it. We found 3 clusters of patients, and 3 clusters of B-cells.
require(pheatmap, quietly = T)
require(RColorBrewer, quietly = T)
colorfun <- function(nlevels, ...){
if(nlevels > 10) return(standardColors(nlevels))
if(nlevels <=10) return(pal_d3(...)(nlevels))
}
annotation.row <- data.frame("Molecule" = factor(c("none", rep("B-cell",29), rep("cytokine",38))))
annotation.col <- df.raw[,c(1,4,5,6,7,8,9,10,11,12,13)]
annotation.colors <- lapply(colnames(annotation.col), function(nam){
colrs <- colorfun(nlevels(annotation.col[[nam]]))
names(colrs) <- levels(annotation.col[[nam]])
return(colrs)
})
names(annotation.colors) <- colnames(annotation.col)
annotation.colors[["Molecule"]] <- colorfun(nlevels(annotation.row$Molecule))
names(annotation.colors[["Molecule"]]) <- levels(annotation.row$Molecule)
df.patient <- double_standardize(t(na.omit(df.raw[,c(3,bcellcyto)])))
rownames(annotation.row) <- rownames(df.patient)
pheatmap(df.patient,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
breaks = c(seq(-max(abs(df.patient), na.rm=T), 0, length.out = 50), seq(0.001, max(abs(df.patient), na.rm=T), length.out = 50)),
#scale = "row",
fontsize = 7,
cutree_rows = 4,
cutree_cols = 4,
cluster_cols = T,
cluster_rows = T,
annotation_col = annotation.col,
annotation_row = annotation.row,
annotation_colors = annotation.colors,
clustering_method = "ward.D2"
)

Metadata associations
We computed the statistical associations between sample groups specified by the metadata.
Survival
We computed metadata factors that were statistically associated with survival.
df.factors <- df[match(levels(df$PatientID), df$PatientID),]
isfactor <- which(sapply(df.factors, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Survival","Outcome", "LowIntermacs"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$Survival)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
4.900
|
0.7440
|
39.00
|
0.0974
|
0.681
|
RVAD
|
|
2
|
NA
|
NA
|
NA
|
0.354
|
0.823
|
Device Type
|
|
3
|
2.610
|
0.1070
|
188.00
|
0.569
|
0.823
|
Sensitized
|
|
4
|
NA
|
NA
|
NA
|
0.616
|
0.823
|
InterMACS
|
|
5
|
0.677
|
0.0853
|
5.94
|
0.674
|
0.823
|
Sex
|
|
6
|
1.480
|
0.2450
|
9.82
|
0.705
|
0.823
|
AgeGreater60
|
|
7
|
0.879
|
0.0656
|
7.89
|
1
|
1
|
VAD Indication
|
InterMACS
We computed metadata factors that were statistically associated with a binarized InterMACS score.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("LowIntermacs", "InterMACS"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$LowIntermacs)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
NA
|
NA
|
NA
|
0.135
|
0.983
|
Device Type
|
|
2
|
3.070
|
0.520
|
21.10
|
0.246
|
0.983
|
AgeGreater60
|
|
3
|
0.583
|
0.062
|
5.43
|
0.653
|
1
|
VAD Indication
|
|
4
|
NA
|
NA
|
NA
|
0.689
|
1
|
Outcome
|
|
5
|
1.830
|
0.288
|
14.60
|
0.689
|
1
|
RVAD
|
|
6
|
1.310
|
0.116
|
15.90
|
1
|
1
|
Sensitized
|
|
7
|
1.210
|
0.140
|
9.40
|
1
|
1
|
Sex
|
|
8
|
0.956
|
0.153
|
6.40
|
1
|
1
|
Survival
|
Sensitization
We computed metadata factors that were statistically associated with sensitization.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Sensitized"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$Sensitized)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
0.127
|
0.00192
|
1.99
|
0.119
|
1
|
Sex
|
|
2
|
2.610
|
0.10700
|
188.00
|
0.569
|
1
|
Survival
|
|
3
|
NA
|
NA
|
NA
|
0.569
|
1
|
Outcome
|
|
4
|
0.426
|
0.02530
|
5.03
|
0.608
|
1
|
RVAD
|
|
5
|
NA
|
NA
|
NA
|
0.843
|
1
|
Device Type
|
|
6
|
1.310
|
0.11600
|
15.90
|
1
|
1
|
LowIntermacs
|
|
7
|
1.230
|
0.10100
|
15.40
|
1
|
1
|
AgeGreater60
|
|
8
|
NA
|
NA
|
NA
|
1
|
1
|
InterMACS
|
|
9
|
0.000
|
0.00000
|
Inf
|
1
|
1
|
VAD Indication
|
Sex
We computed metadata factors that were statistically associated with sex.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Sex"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$Sex)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
0.127
|
0.00192
|
1.99
|
0.119
|
0.89
|
Sensitized
|
|
2
|
3.860
|
0.48600
|
49.80
|
0.198
|
0.89
|
AgeGreater60
|
|
3
|
NA
|
NA
|
NA
|
0.368
|
1
|
Outcome
|
|
4
|
NA
|
NA
|
NA
|
0.447
|
1
|
Device Type
|
|
5
|
0.677
|
0.08530
|
5.94
|
0.674
|
1
|
RVAD
|
|
6
|
0.677
|
0.08530
|
5.94
|
0.674
|
1
|
Survival
|
|
7
|
1.210
|
0.14000
|
9.40
|
1
|
1
|
LowIntermacs
|
|
8
|
NA
|
NA
|
NA
|
1
|
1
|
InterMACS
|
|
9
|
1.840
|
0.15200
|
103.00
|
1
|
1
|
VAD Indication
|
Age
We computed metadata factors that were statistically associated with age.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("AgeGreater60"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$AgeGreater60)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
0.228
|
0.0274
|
1.45
|
0.114
|
0.442
|
RVAD
|
|
2
|
NA
|
NA
|
NA
|
0.128
|
0.442
|
InterMACS
|
|
3
|
NA
|
NA
|
NA
|
0.159
|
0.442
|
Outcome
|
|
4
|
3.860
|
0.4860
|
49.80
|
0.198
|
0.442
|
Sex
|
|
5
|
3.070
|
0.5200
|
21.10
|
0.246
|
0.442
|
LowIntermacs
|
|
6
|
NA
|
NA
|
NA
|
0.35
|
0.525
|
Device Type
|
|
7
|
1.950
|
0.2230
|
25.90
|
0.655
|
0.794
|
VAD Indication
|
|
8
|
1.480
|
0.2450
|
9.82
|
0.705
|
0.794
|
Survival
|
|
9
|
1.230
|
0.1010
|
15.40
|
1
|
1
|
Sensitized
|
VAD Indication
We computed metadata factors that were statistically associated with VAD Indication.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("VAD Indication"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$`VAD Indication`)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
NA
|
NA
|
NA
|
0.000242
|
0.00217
|
Outcome
|
|
2
|
0.000
|
0.0000
|
1.34
|
0.0619
|
0.278
|
RVAD
|
|
3
|
0.583
|
0.0620
|
5.43
|
0.653
|
1
|
LowIntermacs
|
|
4
|
1.950
|
0.2230
|
25.90
|
0.655
|
1
|
AgeGreater60
|
|
5
|
NA
|
NA
|
NA
|
0.837
|
1
|
InterMACS
|
|
6
|
NA
|
NA
|
NA
|
0.877
|
1
|
Device Type
|
|
7
|
1.840
|
0.1520
|
103.00
|
1
|
1
|
Sex
|
|
8
|
0.000
|
0.0000
|
Inf
|
1
|
1
|
Sensitized
|
|
9
|
0.879
|
0.0656
|
7.89
|
1
|
1
|
Survival
|
RVAD
We computed metadata factors that were statistically associated with RVAD.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("RVAD"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$RVAD)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
NA
|
NA
|
NA
|
0.00868
|
0.0782
|
Device Type
|
|
2
|
0.000
|
0.0000
|
1.34
|
0.0619
|
0.205
|
VAD Indication
|
|
3
|
NA
|
NA
|
NA
|
0.0739
|
0.205
|
Outcome
|
|
4
|
4.900
|
0.7440
|
39.00
|
0.0974
|
0.205
|
Survival
|
|
5
|
0.228
|
0.0274
|
1.45
|
0.114
|
0.205
|
AgeGreater60
|
|
6
|
NA
|
NA
|
NA
|
0.327
|
0.49
|
InterMACS
|
|
7
|
0.426
|
0.0253
|
5.03
|
0.608
|
0.689
|
Sensitized
|
|
8
|
0.677
|
0.0853
|
5.94
|
0.674
|
0.689
|
Sex
|
|
9
|
1.830
|
0.2880
|
14.60
|
0.689
|
0.689
|
LowIntermacs
|
Device-type
We computed metadata factors that were statistically associated with Device Type.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Device Type"))],
function(this_factor){
table(df.factors[,this_factor], df.factors$`Device Type`)
})
fisher.p <- sapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = T, B = 10000)
ft$p
})
fisher.OR.0 <- lapply(contingency, function(this_table){
ft <- fisher.test(this_table, simulate.p.value = T, B = 10000)
if(is.null(ft$estimate)) {
ft$estimate <- NA
ft$conf.int <- c(NA,NA)
}
ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)
qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)
qtable %>%
mutate(
`Factor` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
|
OR
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
Factor
|
|
1
|
NA
|
NA
|
NA
|
0.0089
|
0.0801
|
RVAD
|
|
2
|
NA
|
NA
|
NA
|
0.139
|
0.531
|
LowIntermacs
|
|
3
|
NA
|
NA
|
NA
|
0.274
|
0.531
|
InterMACS
|
|
4
|
NA
|
NA
|
NA
|
0.346
|
0.531
|
Outcome
|
|
5
|
NA
|
NA
|
NA
|
0.352
|
0.531
|
AgeGreater60
|
|
6
|
NA
|
NA
|
NA
|
0.354
|
0.531
|
Survival
|
|
7
|
NA
|
NA
|
NA
|
0.443
|
0.569
|
Sex
|
|
8
|
NA
|
NA
|
NA
|
0.835
|
0.874
|
Sensitized
|
|
9
|
NA
|
NA
|
NA
|
0.874
|
0.874
|
VAD Indication
|
Variability of MCS devices
PCA
Using Principal Component Analysis (PCA), we saw large variability between device types, compared to the variability within device types. We also saw large variability between individual patients. None of the other features were clearly separable.
suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)
isna <- unique(unlist(apply(df[,c(bcellcyto)], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df[-isna, c(bcellcyto)]), center = TRUE, scale. = TRUE)
colorfun <- function(grouping, ...){
if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
}
plotvars <- names(df)[c(11,1:13)]
plots.pca <- mclapply(plotvars, function(this_var){
this_groups <- df[-isna, this_var]
if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
ggbiplot(pca,
groups = this_groups,
ellipse = TRUE,
alpha = 0.3,
varname.size = 1.2) +
colorfun(this_groups, name = this_var) +
ggtitle(paste0(this_var, " variability")) +
theme_classic()
}, mc.cores = detectCores()-1)
names(plots.pca) <- plotvars
#plots.pca$PatientID
#plots.pca$`Device Type`
for(ii in 1:length(plots.pca)){
cat(" \n###", names(plots.pca)[ii], "\n")
suppressWarnings(print(plots.pca[[ii]]))
cat(" \n")
}
Device Type

PatientID

Time

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Outcome

Survival

Fisher’s exact test
We analyzed the dependency of device types on the other discrete variables using Fisher’s exact test.
isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[names(isfactor) != "Device Type"],
function(this_factor){
table(df.factors[,this_factor], df.factors$`Device Type`)
})
fisher.p <- sapply(contingency, function(this_table){
fisher.test(this_table, simulate.p.value = T, B = 10000)$p
})
fisher.q <- p.adjust(fisher.p, method = "BH")
signif.ix <- which(fisher.p < 0.05)
signif.order <- sort(fisher.q[signif.ix], index.return = T)$ix
for(this_ix in signif.ix[signif.order]){
cat(" \n###", names(contingency)[this_ix], "\n")
cat(paste0("Benjamini-Hochberg qvalue = ", signif(fisher.q[this_ix], 2)),
". \n")
print(kable(contingency[this_ix][[1]], row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
)
cat(" \n")
}
RVAD
Benjamini-Hochberg qvalue = 0.077 .
|
|
HMII
|
CMAG
|
HVAD
|
PVAD
|
TAH
|
|
No
|
15
|
1
|
2
|
0
|
0
|
|
Yes
|
5
|
0
|
0
|
3
|
2
|
One-way repeated measures anova
We first analyzed the differences in B-cell levels across device types using a one-way repeated measures anova. Here we report any variables that had a statistically significant variance (\(p<0.05\)) across devices, time, or their interaction.
suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))
df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)
varnames <- colnames(df.device)[c(bcellcyto)]
models.device <- mclapply(varnames, function(this_var){
this_formula <- as.formula(paste0(this_var, " ~ Device.Type + (1|PatientID)"))
invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
this_anova <- Anova(this_model, type = 2)
pvals <- this_anova$`Pr(>Chisq)`[c(1)]
return(list(model = this_model,
pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[c(bcellcyto)]
pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- colnames(df)[c(bcellcyto)]
colnames(pvals) <- c("pvalue")
qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[c(bcellcyto)]
colnames(qBH) <- c("qvalue")
sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- cbind(pvals,qBH)[sigvars,,drop=F][order(apply(pvals[sigvars,,drop=F], 1, min)),,drop=F]
qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
mutate(
`B-cell` = row.names(.),
pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
) %>%
kable( escape = F,
digits = 20,
row.names = T,
caption = "Significant device-type q-values") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 12) %>%
scroll_box(width = "100%")
Significant device-type q-values
|
|
pvalue
|
qvalue
|
B-cell
|
|
1
|
1.2e-23
|
8e-22
|
IL-4
|
|
2
|
1.7e-16
|
5.8e-15
|
IL-1RA
|
|
3
|
0.00013
|
0.003
|
MCP-1
|
|
4
|
0.011
|
0.18
|
CD27-IgD+ mature naive
|
|
5
|
0.023
|
0.27
|
MIP-1a
|
|
6
|
0.026
|
0.27
|
IL-6
|
|
7
|
0.029
|
0.27
|
IL-8
|
|
8
|
0.045
|
0.35
|
CD19+CD5+
|
We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the fit. Note that the reference level for the devices is the HeartMate-II (HMII).
require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))
invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}
plots.ts <- mclapply(rownames(qtable), function(this_var){
lapply(groups, function(this_groups){
ggplot(subset(df.long, df.long$variable == this_var)) +
aes(x = Time, y = value, group = PatientID) +
aes_string(color = this_groups, fill = this_groups) +
geom_line(alpha = 0) +
geom_point(alpha = 0) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) +
stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) +
#stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
scale_color_d3() + scale_fill_d3() +
xlab("Time (days after surgery)") +
ylab(this_var) +
ggtitle(paste(this_var)) +
theme_classic()
})
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)
for(ii in c(1:length(plots.ts))){
for(jj in 1:length(plots.ts[[ii]])){
sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
cat(" \n###", rownames(qtable)[ii], "\n")
print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
)
cat(" \n")
suppressWarnings(print(plots.ts[[ii]][[jj]]))
cat(" \n")
}
}
IL-4
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH
|
220.662
|
21.04556
|
96.0249
|
10.48497
|
0
|

IL-1RA
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
1331.313
|
152.1179
|
15.06184
|
8.751848
|
3e-07
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
1865.109
|
407.1979
|
16.04335
|
4.580351
|
0.000306
|

CD27-IgD+ mature naive
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD
|
-33.85486
|
10.06687
|
23.54822
|
-3.362998
|
0.0026287
|

MIP-1a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeHVAD
|
32.60928
|
12.418797
|
15.28209
|
2.625801
|
0.0188723
|
|
Device.TypePVAD
|
16.86374
|
7.943291
|
17.68110
|
2.123017
|
0.0481402
|

IL-6
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
189.3771
|
68.51606
|
12.66841
|
2.763981
|
0.0164291
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
77.25016
|
33.74592
|
15.23866
|
2.289171
|
0.0367422
|
|
Device.TypeTAH
|
73.38816
|
33.74592
|
15.23866
|
2.174727
|
0.0457932
|

CD19+CD5+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD
|
23.16366
|
8.615765
|
23.18582
|
2.688521
|
0.0130627
|

Linear mixed-effect model
We next analyzed the differences in B-cell levels across device types using a linear mixed effect model with time as a continuous variable, and included the interaction term. Here we report variables that had a statistically significant variance (Benjamini-Hochberg \(p<0.05\)) across devices, time, or their interaction.
suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))
df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)
varnames <- colnames(df.device)[bcellcyto]
models.device <- mclapply(varnames, function(this_var){
this_formula <- as.formula(paste0(this_var, " ~ Device.Type * Time + (1|PatientID)"))
invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
this_anova <- Anova(this_model, type = 2)
pvals <- this_anova$`Pr(>Chisq)`[c(1,2,3)]
return(list(model = this_model,
pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[bcellcyto]
pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- varnames
colnames(pvals) <- c("Device", "Time", "Device:Time")
qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[bcellcyto]
colnames(qBH) <- colnames(pvals)
sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- qBH[sigvars,][order(apply(qBH[sigvars,], 1, min)),]
qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
mutate(
`B-cell` = row.names(.),
Device = cell_spec(Device, color = ifelse(qtable$Device > 0.05, "grey", "red")),
`Device:Time` = cell_spec(`Device:Time`, color = ifelse(qtable$`Device:Time` > 0.05, "grey", "red")),
`Time` = cell_spec(`Time`, color = ifelse(qtable$`Time` > 0.05, "grey", "red"))
) %>%
kable( escape = F,
digits = 20,
row.names = T,
caption = "Significant device-type q-values") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 12) %>%
scroll_box(width = "100%")
Significant device-type q-values
|
|
Device
|
Time
|
Device:Time
|
B-cell
|
|
1
|
8.9e-76
|
0.0033
|
1e-40
|
IL-4
|
|
2
|
1.1e-14
|
1
|
0.22
|
IL-1RA
|
|
3
|
0.78
|
0.0013
|
0.0041
|
CD19+CD11b+
|
|
4
|
0.22
|
0.98
|
0.0023
|
MIP-1a
|
|
5
|
0.0033
|
1
|
0.57
|
MCP-1
|
|
6
|
0.98
|
0.019
|
1
|
CD19+CD5+CD11b+
|
|
7
|
0.64
|
0.023
|
1
|
CD268 of +27-38++transitional
|
|
8
|
0.48
|
0.089
|
1
|
CD19+CD268+
|
|
9
|
1
|
0.089
|
0.78
|
IL-1b
|
|
10
|
1
|
0.095
|
0.98
|
CD19+27-38+CD5+transitionals
|
|
11
|
1
|
0.12
|
1
|
CD19 of live lymph
|
|
12
|
0.57
|
0.31
|
0.12
|
CD27+38++plasma blasts
|
|
13
|
0.19
|
0.24
|
0.3
|
CD27-IgD+ mature naive
|
|
14
|
0.57
|
0.22
|
1
|
CD27+IgD- switched memory
|
|
15
|
0.22
|
1
|
0.97
|
IL-6
|
|
16
|
0.34
|
0.22
|
1
|
CD19+CD5+
|
|
17
|
1
|
0.22
|
1
|
CD19+CD5+CD24hi
|
|
18
|
1
|
0.22
|
1
|
IL-3
|
|
19
|
1
|
0.22
|
0.76
|
sCD40L
|
|
20
|
0.98
|
0.23
|
1
|
num lymph
|
|
21
|
0.23
|
1
|
1
|
IL-8
|
We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit. Note that the reference level for the devices is the HeartMate-II (HMII).
require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))
invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}
plots.ts <- mclapply(rownames(qtable), function(this_var){
lapply(groups, function(this_groups){
ggplot(subset(df.long, df.long$variable == this_var)) +
aes(x = Time, y = value, group = PatientID) +
aes_string(color = this_groups, fill = this_groups) +
geom_line(alpha = 0) +
geom_point(alpha = 0) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) +
stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) +
#stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
scale_color_d3() + scale_fill_d3() +
xlab("Time (days after surgery)") +
ylab(this_var) +
ggtitle(paste(this_var)) +
theme_classic()
})
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)
for(ii in c(1:length(plots.ts))){
for(jj in 1:length(plots.ts[[ii]])){
sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
cat(" \n###", rownames(qtable)[ii], "\n")
print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
)
cat(" \n")
suppressWarnings(print(plots.ts[[ii]][[jj]]))
cat(" \n")
}
}
IL-4
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:Time
|
57.66099
|
4.0869
|
90.99768
|
14.10873
|
0
|

IL-1RA
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
1583.30519
|
170.53857
|
23.97074
|
9.284147
|
0.0000000
|
|
Device.TypeCMAG:Time
|
-74.12118
|
22.72778
|
71.75398
|
-3.261260
|
0.0016987
|

CD19+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:Time
|
2.1101624
|
0.5538816
|
89.91926
|
3.809771
|
0.0002541
|
|
Device.TypePVAD:Time
|
0.8054722
|
0.2943289
|
88.29842
|
2.736640
|
0.0075041
|
|
Time
|
0.2628129
|
0.1188074
|
89.80589
|
2.212092
|
0.0294973
|

MIP-1a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeHVAD:Time
|
15.08983
|
3.068267
|
74.37332
|
4.918031
|
5.1e-06
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
2455.3824
|
490.26835
|
32.93458
|
5.008242
|
0.0000181
|
|
Device.TypeCMAG:Time
|
-173.6232
|
81.38512
|
73.35568
|
-2.133353
|
0.0362406
|

CD19+CD5+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.290629
|
0.1150892
|
91.27483
|
2.525249
|
0.0132869
|

CD268 of +27-38++transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
-0.7873216
|
0.3030382
|
89.79933
|
-2.598094
|
0.0109556
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
-0.6425061
|
0.230173
|
88.33548
|
-2.791405
|
0.0064310
|
|
Device.TypePVAD
|
-37.7748686
|
16.138544
|
27.53030
|
-2.340662
|
0.0267323
|

IL-1b
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.3273811
|
0.1608446
|
72.42554
|
2.035387
|
0.0454716
|

CD19+27-38+CD5+transitionals
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.0910247
|
0.0395736
|
89.94149
|
2.30014
|
0.0237538
|

CD19 of live lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
-0.3683497
|
0.140373
|
93.29085
|
-2.624078
|
0.0101509
|

CD27+38++plasma blasts
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD:Time
|
0.2768179
|
0.1033923
|
88.20166
|
2.677355
|
0.0088487
|

CD27-IgD+ mature naive
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD
|
-32.958219
|
10.7368535
|
29.72853
|
-3.069635
|
0.0045463
|
|
Device.TypeTAH:Time
|
-2.247904
|
0.8166692
|
89.34041
|
-2.752527
|
0.0071618
|

CD27+IgD- switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.3654018
|
0.1606941
|
88.62458
|
2.273897
|
0.0253875
|
|
Device.TypePVAD
|
25.0335542
|
10.8348873
|
28.15496
|
2.310458
|
0.0284029
|

IL-6
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
304.8154
|
95.59353
|
46.0739
|
3.188661
|
0.0025701
|

CD19+CD5+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD
|
19.97858
|
9.433345
|
33.29569
|
2.117868
|
0.0417364
|

CD19+CD5+CD24hi
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IL-3
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.2062222
|
0.0939434
|
74.80342
|
2.195175
|
0.0312533
|

sCD40L
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
96.94986
|
42.04762
|
34.74735
|
2.305716
|
0.0272161
|

Two-way repeated measures anova
Finally, we attempted to analyze the differences in B-cell levels across device types using a two-way repeated measures ANOVA. Here we report variables that had a statistically significant variance (\(p<0.05\)) across devices, times, or their interaction. As there were 5 device types and 7 timepoints, but only 166 samples, this model is severely underpowered. The posterior belief in any of these results should therefore be quite small (as a consequence of Bayes rule).
suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))
df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)
varnames <- colnames(df.device)[bcellcyto]
models.device <- mclapply(varnames, function(this_var){
this_formula <- as.formula(paste0(this_var, " ~ Device.Type * factor(Time) + (1|PatientID)"))
invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
this_anova <- Anova(this_model, type = 2)
pvals <- this_anova$`Pr(>Chisq)`[c(1,2,3)]
return(list(model = this_model,
pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[bcellcyto]
pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- varnames
colnames(pvals) <- c("Device", "factor(Time)", "Device:factor(Time)")
qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[bcellcyto]
colnames(qBH) <- colnames(pvals)
sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- qBH[sigvars,][order(apply(qBH[sigvars,], 1, min)),]
qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
mutate(
`B-cell` = row.names(.),
Device = cell_spec(Device, color = ifelse(qtable$Device > 0.05, "grey", "red")),
`Device:factor(Time)` = cell_spec(`Device:factor(Time)`, color = ifelse(qtable$`Device:factor(Time)` > 0.05, "grey", "red")),
`factor(Time)` = cell_spec(`factor(Time)`, color = ifelse(qtable$`factor(Time)` > 0.05, "grey", "red"))
) %>%
kable( escape = F,
digits = 20,
row.names = T,
caption = "Significant device-type q-values") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 12) %>%
scroll_box(width = "100%")
Significant device-type q-values
|
|
Device
|
factor(Time)
|
Device:factor(Time)
|
B-cell
|
|
1
|
1.1e-81
|
3.3e-07
|
1.4e-123
|
IL-4
|
|
2
|
1.1e-14
|
0.55
|
0.022
|
IL-1RA
|
|
3
|
0.2
|
0.09
|
1.7e-10
|
CD19+CD5+
|
|
4
|
0.13
|
0.79
|
2.5e-09
|
MIP-1a
|
|
5
|
0.19
|
0.47
|
3.5e-06
|
IL-6
|
|
6
|
0.42
|
0.046
|
5e-06
|
CD27+IgD- switched memory
|
|
7
|
0.66
|
0.093
|
7.2e-05
|
CD19+CD27+
|
|
8
|
0.66
|
0.38
|
7.2e-05
|
CD27-IgD- switched memory
|
|
9
|
0.0017
|
0.26
|
2e-04
|
MCP-1
|
|
10
|
0.69
|
0.00038
|
0.0017
|
CD19+CD11b+
|
|
11
|
0.092
|
0.19
|
0.0037
|
CD27-IgD+ mature naive
|
|
12
|
1
|
0.0063
|
1
|
CD19 of live lymph
|
|
13
|
0.68
|
0.0063
|
1
|
G-CSF
|
|
14
|
0.19
|
0.012
|
1
|
IL-8
|
|
15
|
1
|
1
|
0.013
|
CD19CD24hiCD38-memory
|
|
16
|
1
|
0.04
|
1
|
IL-10
|
|
17
|
0.32
|
0.046
|
1
|
CD27+IgD-IgM+ switched memory
|
|
18
|
1
|
0.053
|
1
|
IL-1b
|
|
19
|
1
|
1
|
0.063
|
CD19+24dim38dim naive mature
|
|
20
|
1
|
0.66
|
0.08
|
CD19+CD27+CD24hi
|
|
21
|
0.66
|
0.083
|
1
|
CD268 of +27-38++transitional
|
|
22
|
0.96
|
0.14
|
0.7
|
CD19+CD5+CD11b+
|
|
23
|
0.37
|
0.19
|
0.15
|
CD19+CD268+
|
|
24
|
1
|
0.16
|
1
|
Eotaxin
|
|
25
|
1
|
0.19
|
0.97
|
MDC
|
|
26
|
0.53
|
0.26
|
0.21
|
CD27+38++plasma blasts
|
|
27
|
0.26
|
0.21
|
0.94
|
IL-15
|
|
28
|
0.23
|
0.35
|
1
|
TNF-b
|
|
29
|
1
|
0.23
|
1
|
IL-5
|
We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit. Note that the reference level for the time comparisons is timepoint 0, and the reference level for the devices is the HeartMate-II (HMII).
require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))
invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}
plots.ts <- mclapply(rownames(qtable), function(this_var){
lapply(groups, function(this_groups){
ggplot(subset(df.long, df.long$variable == this_var)) +
aes(x = Time, y = value, group = PatientID) +
aes_string(color = this_groups, fill = this_groups) +
geom_line(alpha = 0) +
geom_point(alpha = 0) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) +
stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) +
#stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
scale_color_d3() + scale_fill_d3() +
xlab("Time (days after surgery)") +
ylab(this_var) +
ggtitle(paste(this_var)) +
theme_classic()
})
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)
for(ii in c(1:length(plots.ts))){
for(jj in 1:length(plots.ts[[ii]])){
sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
cat(" \n###", rownames(qtable)[ii], "\n")
print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),], row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
)
cat(" \n")
suppressWarnings(print(plots.ts[[ii]][[jj]]))
cat(" \n")
}
}
IL-4
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)5
|
473.44988
|
23.37471
|
56.29429
|
20.254795
|
0.0000000
|
|
Device.TypeTAH:factor(Time)8
|
398.35765
|
23.34425
|
56.24413
|
17.064486
|
0.0000000
|
|
Device.TypeTAH:factor(Time)3
|
220.44490
|
23.34425
|
56.24413
|
9.443220
|
0.0000000
|
|
Device.TypeTAH:factor(Time)1
|
53.48854
|
23.37471
|
56.29429
|
2.288309
|
0.0258953
|

IL-1RA
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG
|
1277.2905
|
193.4375
|
37.65943
|
6.603117
|
0.0000001
|
|
Device.TypeCMAG:factor(Time)1
|
693.5273
|
189.4589
|
57.30055
|
3.660569
|
0.0005499
|
|
Device.TypePVAD:factor(Time)5
|
-268.5598
|
130.7894
|
58.77891
|
-2.053375
|
0.0444945
|

CD19+CD5+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)5
|
79.25674
|
11.074689
|
67.92417
|
7.156566
|
0.0000000
|
|
Device.TypePVAD
|
35.52841
|
9.894557
|
43.53672
|
3.590703
|
0.0008326
|
|
Device.TypePVAD:factor(Time)3
|
-25.18076
|
8.751886
|
71.43189
|
-2.877181
|
0.0052872
|
|
Device.TypePVAD:factor(Time)5
|
-31.58267
|
11.241069
|
73.06155
|
-2.809579
|
0.0063611
|
|
Device.TypePVAD:factor(Time)8
|
-20.60508
|
7.572762
|
69.16235
|
-2.720946
|
0.0082276
|

MIP-1a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeHVAD:factor(Time)8
|
159.25574
|
23.02507
|
59.36219
|
6.916624
|
0.0000000
|
|
Device.TypePVAD:factor(Time)3
|
46.08958
|
15.73936
|
61.98365
|
2.928301
|
0.0047623
|
|
Device.TypeHVAD:factor(Time)1
|
47.74151
|
23.05589
|
59.40146
|
2.070686
|
0.0427425
|

IL-6
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG:factor(Time)3
|
798.6437
|
139.2084
|
56.45800
|
5.737037
|
0.0000004
|
|
Device.TypeTAH:factor(Time)1
|
310.2112
|
139.3936
|
56.50358
|
2.225434
|
0.0300595
|
|
Device.TypeCMAG:factor(Time)1
|
291.1312
|
139.3936
|
56.50358
|
2.088556
|
0.0412650
|

CD27+IgD- switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD:factor(Time)3
|
-44.11347
|
8.159997
|
70.32571
|
-5.406064
|
0.0000008
|
|
Device.TypePVAD
|
46.55839
|
10.997051
|
35.92275
|
4.233716
|
0.0001521
|
|
Device.TypePVAD:factor(Time)8
|
-25.13824
|
7.037695
|
68.87484
|
-3.571943
|
0.0006518
|
|
Device.TypePVAD:factor(Time)5
|
-35.76427
|
10.505189
|
71.34328
|
-3.404439
|
0.0010914
|
|
Device.TypeHVAD:factor(Time)1
|
22.95446
|
9.340788
|
68.84096
|
2.457443
|
0.0165160
|
|
Device.TypeTAH:factor(Time)1
|
19.80313
|
9.340788
|
68.84096
|
2.120071
|
0.0376068
|

CD19+CD27+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD:factor(Time)3
|
-44.86147
|
9.352169
|
70.81526
|
-4.796905
|
0.0000087
|
|
Device.TypePVAD
|
40.90474
|
11.677321
|
38.82944
|
3.502922
|
0.0011749
|
|
Device.TypePVAD:factor(Time)5
|
-36.38395
|
12.029880
|
72.05224
|
-3.024465
|
0.0034501
|
|
Device.TypePVAD:factor(Time)8
|
-22.52547
|
8.075489
|
69.06666
|
-2.789363
|
0.0068193
|
|
Device.TypeHVAD:factor(Time)1
|
26.28504
|
10.718455
|
69.03750
|
2.452316
|
0.0167261
|

CD27-IgD- switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)5
|
71.68679
|
13.81377
|
66.76113
|
5.189516
|
0.0000021
|
|
Device.TypeTAH:factor(Time)14
|
34.72518
|
15.34586
|
76.65481
|
2.262837
|
0.0264767
|
|
Device.TypePVAD:factor(Time)3
|
-22.85502
|
10.65984
|
75.69366
|
-2.144030
|
0.0352412
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG:factor(Time)3
|
2642.6492
|
606.3312
|
58.57383
|
4.358425
|
0.0000536
|
|
Device.TypeCMAG
|
1709.5677
|
556.6078
|
48.15330
|
3.071405
|
0.0034988
|
|
factor(Time)1
|
363.0153
|
155.7998
|
59.46423
|
2.330012
|
0.0232187
|
|
Device.TypeCMAG:factor(Time)8
|
-1265.9221
|
606.3312
|
58.57383
|
-2.087839
|
0.0411707
|

CD19+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)14
|
52.95791
|
10.085405
|
72.02647
|
5.250946
|
0.0000015
|
|
Device.TypeTAH:factor(Time)1
|
21.89638
|
8.043661
|
70.05708
|
2.722190
|
0.0081767
|
|
Device.TypePVAD:factor(Time)14
|
17.80531
|
7.197456
|
72.09567
|
2.473834
|
0.0157228
|
|
Device.TypeTAH:factor(Time)3
|
16.76692
|
8.048562
|
70.13505
|
2.083219
|
0.0408780
|

CD27-IgD+ mature naive
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)5
|
-49.338009
|
12.632153
|
68.31654
|
-3.905748
|
0.0002179
|
|
Device.TypePVAD
|
-40.588774
|
11.742845
|
41.95254
|
-3.456469
|
0.0012664
|
|
Device.TypeTAH:factor(Time)14
|
-42.518754
|
14.393799
|
71.18903
|
-2.953963
|
0.0042493
|
|
Device.TypeTAH:factor(Time)1
|
-29.588950
|
11.470396
|
69.40469
|
-2.579593
|
0.0120122
|
|
factor(Time)8
|
-7.421838
|
2.996063
|
68.79574
|
-2.477197
|
0.0157031
|
|
Device.TypeHVAD:factor(Time)1
|
-23.895494
|
11.470396
|
69.40469
|
-2.083232
|
0.0409155
|

CD19 of live lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)1
|
8.670607
|
2.838543
|
69.69727
|
3.054598
|
0.0031913
|
|
Device.TypeHVAD:factor(Time)5
|
18.621453
|
8.106642
|
68.64255
|
2.297061
|
0.0246707
|
|
Device.TypeHVAD:factor(Time)3
|
16.313101
|
7.998551
|
68.60745
|
2.039507
|
0.0452529
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)1
|
75.26281
|
30.59711
|
60.67210
|
2.459801
|
0.0167667
|
|
Device.TypeTAH:factor(Time)1
|
251.33419
|
119.55029
|
59.29771
|
2.102330
|
0.0397798
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)1
|
48.21632
|
15.8183
|
59.67957
|
3.048135
|
0.0034288
|

CD19CD24hiCD38-memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)5
|
-22.909686
|
6.066396
|
68.15378
|
-3.776490
|
0.0003363
|
|
Device.TypeHVAD:factor(Time)1
|
12.942595
|
5.519995
|
68.69011
|
2.344675
|
0.0219407
|
|
factor(Time)1
|
-3.388861
|
1.568014
|
68.34505
|
-2.161244
|
0.0341830
|

IL-10
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)1
|
162.0198
|
59.75738
|
58.42885
|
2.711294
|
0.0087864
|

CD27+IgD-IgM+ switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)5
|
10.711338
|
3.308708
|
71.05852
|
3.237317
|
0.0018346
|
|
factor(Time)3
|
8.467757
|
3.003200
|
71.42371
|
2.819578
|
0.0062189
|
|
factor(Time)14
|
8.273763
|
3.705650
|
70.67032
|
2.232742
|
0.0287354
|

IL-1b
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+24dim38dim naive mature
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypePVAD:factor(Time)3
|
-51.79949
|
13.95422
|
76.12811
|
-3.712101
|
0.0003892
|

CD19+CD27+CD24hi
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)5
|
-22.54139
|
9.215036
|
68.32804
|
-2.446154
|
0.0170182
|
|
factor(Time)8
|
4.88642
|
2.184243
|
68.95940
|
2.237123
|
0.0285135
|
|
Device.TypeHVAD:factor(Time)1
|
18.31135
|
8.355636
|
69.79004
|
2.191497
|
0.0317555
|

CD268 of +27-38++transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)8
|
-13.21005
|
5.828370
|
69.05531
|
-2.266508
|
0.0265563
|
|
factor(Time)14
|
-16.08481
|
7.874549
|
69.65371
|
-2.042632
|
0.0448754
|

CD19+CD5+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)5
|
-66.88010
|
17.16044
|
67.82056
|
-3.897342
|
0.0002253
|
|
Device.TypePVAD
|
-39.35919
|
17.41639
|
37.46863
|
-2.259893
|
0.0297274
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

MDC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)1
|
-116.6333
|
46.70457
|
59.46922
|
-2.497257
|
0.0153028
|

CD27+38++plasma blasts
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeTAH:factor(Time)1
|
7.049114
|
2.979632
|
70.86460
|
2.365767
|
0.0207319
|
|
Device.TypeCMAG
|
7.332777
|
3.630995
|
44.46773
|
2.019495
|
0.0494876
|

IL-15
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Device.TypeCMAG:factor(Time)3
|
15.986654
|
6.525592
|
58.14190
|
2.44984
|
0.0173241
|
|
factor(Time)8
|
3.382095
|
1.641873
|
58.49596
|
2.05990
|
0.0438665
|

TNF-b
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)1
|
-48.00227
|
17.12941
|
58.05021
|
-2.802330
|
0.0068843
|
|
factor(Time)3
|
-45.91700
|
16.76077
|
57.61190
|
-2.739553
|
0.0081753
|
|
factor(Time)5
|
-34.81589
|
17.12941
|
58.05021
|
-2.032521
|
0.0466840
|

IL-5
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)1
|
-8.197292
|
2.673786
|
59.39955
|
-3.065800
|
0.0032645
|
|
factor(Time)3
|
-6.821442
|
2.618986
|
58.78438
|
-2.604612
|
0.0116301
|
|
factor(Time)8
|
-6.460310
|
2.618986
|
58.78438
|
-2.466721
|
0.0165701
|
|
factor(Time)5
|
-6.183556
|
2.673786
|
59.39955
|
-2.312659
|
0.0242238
|

HeartMate-II analysis
The HeartMate-II (HMII) recipients were the largest group, and we analyzed them by themselves due to the previously observed variability across devices.
require(reshape2, quietly = T)
df.HMII <- subset(df, df$`Device Type`=="HMII")
df.long <- melt(df.HMII, id.vars = colnames(df)[1:13])
groups <- make.names(c("AgeGreater60",
"Sex",
"LowIntermacs",
"RVAD",
"Sensitized",
"VAD Indication",
#"Device Type",
"Survival",
"Outcome"))
names(df.long) <- make.names(names(df.long))
plots.ts <- mclapply(unique(df.long$variable), function(this_var){
lapply(groups, function(this_groups){
ggplot(subset(df.long, df.long$variable == this_var)) +
aes(x = Time, y = value, group = PatientID) +
aes_string(color = this_groups, fill = this_groups) +
geom_line(alpha = 0) +
geom_point(alpha = 0) +
#stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) +
stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) +
stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) +
scale_color_aaas() + scale_fill_aaas() +
xlab("Time (days after surgery)") +
ylab(this_var) +
ggtitle(paste(this_var)) +
theme_classic()
})
}, mc.cores = detectCores()-1)
names(plots.ts) <- unique(df.long$variable)
# for(ii in c(1:length(plots.ts))){
# for(jj in 1:length(plots.ts[[ii]])){
# suppressWarnings(print(plots.ts[[ii]][[jj]]))
# }
# }
PCA
Using PCA, we found large variability between individual patients, compared to the variability within individual patients. None of the other features were clearly separable.
suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)
# Efron's double standardization
double_standardize <- function(x, niter = 100) {
for(i in 1:niter) x <- t(scale(t(scale(x))))
return(as.data.frame(x))
}
isna <- unique(unlist(apply(df.HMII[,bcellcyto], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df.HMII[-isna, bcellcyto]), center = TRUE, scale. = TRUE)
colorfun <- function(grouping, ...){
if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
}
plots.pca <- mclapply(names(df.HMII)[1:13], function(this_var){
this_groups <- df.HMII[-isna, this_var]
if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
ggbiplot(pca,
groups = this_groups,
ellipse = TRUE,
alpha = 0.3,
varname.size = 1.2) +
colorfun(this_groups, name = this_var) +
ggtitle(paste0(this_var, " variability")) +
theme_classic()
}, mc.cores = detectCores()-1)
names(plots.pca) <- names(df.HMII)[1:13]
#plots.pca$PatientID
#plots.pca$`Device Type`
for(ii in 1:length(plots.pca)){
cat(" \n###", names(plots.pca)[ii], "\n")
suppressWarnings(print(plots.pca[[ii]]))
cat(" \n")
}
PatientID

Time

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Outcome

Survival

One-way repeated measures anova
We analyzed the differences in B-cell levels for various features using a one-way repeated measures anova. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.
suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)
df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)
groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]
bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]
models.b <- mclapply(groupvars, function(this_groupvar){
models.bcells <- lapply(bcells, function(this_bcell){
this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar,
" + (1|PatientID)"))
suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
this_anova <- Anova(this_model, type = 2)
this_pvalues <- this_anova$`Pr(>Chisq)`
names(this_pvalues) <- rownames(this_anova)
#return(this_pvalues)
return(list(model = this_model,
pvals = this_pvalues))
})
names(models.bcells) <- colnames(df)[bcellcyto]
pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
rownames(pvalues) <- bcells
#return(pvalues)
return(list(model = models.bcells,
pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1)]))
# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix),
# method = "BH"),
# nrow = nrow(pvals.matrix),
# ncol = ncol(pvals.matrix),
# byrow = F)
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]
# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]
pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]
kable(shortlist,
digits = 3,
row.names = T,
caption = "Significant results") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 10) %>%
scroll_box(width = "100%")
Significant results
|
|
B-cell
|
parameter
|
qvalue
|
|
163
|
CD19+27+IgD-38++IgG ASC
|
LowIntermacs
|
0.208
|
|
167
|
TNF-a
|
LowIntermacs
|
0.266
|
|
456
|
MCP-1
|
Outcome
|
0.267
|
|
70
|
lymph
|
Sex
|
0.270
|
|
49
|
IL-8
|
AgeGreater60
|
0.271
|
|
46
|
IL-15
|
AgeGreater60
|
0.271
|
|
2
|
num lymph
|
AgeGreater60
|
0.299
|
|
3
|
lymph
|
AgeGreater60
|
0.367
|
|
523
|
MCP-1
|
Survival
|
0.371
|
|
416
|
CD27+IgD+ unswitched memory
|
Outcome
|
0.395
|
|
396
|
G-CSF
|
VAD.Indication
|
0.483
|
|
83
|
CD27+IgD+IgM+ nonswitched memory
|
Sex
|
0.505
|
|
33
|
TNF-a
|
AgeGreater60
|
0.506
|
|
415
|
CD27-IgD- switched memory
|
Outcome
|
0.507
|
|
51
|
Eotaxin
|
AgeGreater60
|
0.521
|
|
215
|
CD27+IgD+ unswitched memory
|
RVAD
|
0.525
|
|
29
|
CD19+27+IgD-38++IgG ASC
|
AgeGreater60
|
0.527
|
|
20
|
CD19CD24hiCD38-memory
|
AgeGreater60
|
0.530
|
|
137
|
lymph
|
LowIntermacs
|
0.530
|
|
93
|
CD19+CD27+CD24hi
|
Sex
|
0.532
|
|
118
|
Eotaxin
|
Sex
|
0.533
|
|
418
|
CD27+IgD+IgM+ nonswitched memory
|
Outcome
|
0.536
|
|
116
|
IL-8
|
Sex
|
0.536
|
We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above one-way anova. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.
require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
this_group <-siggroups[ii]
this_bcell <- as.character(shortlist$`B-cell`[ii])
cat(" \n###", as.character(shortlist$`B-cell`[ii]), "\n")
sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
)
cat(" \n")
suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
cat(" \n")
}
CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh
|
2.078718
|
0.60011
|
17.90374
|
3.463894
|
0.0027878
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh
|
18.20785
|
6.096687
|
14.89374
|
2.986515
|
0.0092805
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied
|
498.6263
|
225.4061
|
12.04031
|
2.212125
|
0.0470293
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
-18.44662
|
6.370603
|
17.92503
|
-2.895584
|
0.0096664
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
44.84973
|
15.53423
|
14.72595
|
2.887155
|
0.0114543
|

IL-15
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
6.463922
|
2.256074
|
13.72298
|
2.86512
|
0.0126788
|

num lymph
lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
-15.22801
|
5.790664
|
17.53478
|
-2.629751
|
0.0172536
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Survivaldead
|
466.6129
|
178.0397
|
14.27031
|
2.620836
|
0.0198971
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT
|
16.53063
|
6.610333
|
15.90681
|
2.500726
|
0.0237124
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT
|
-71.81929
|
31.45987
|
13.6818
|
-2.282886
|
0.0389804
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
-16.26667
|
7.449772
|
17.76457
|
-2.183512
|
0.0426655
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
15.47275
|
7.106515
|
15.54261
|
2.177263
|
0.045244
|

CD27-IgD- switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19CD24hiCD38-memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+CD27+CD24hi
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

Linear mixed-effect model
We analyzed the differences in B-cell levels for various features using a linear mixed effect model, with time as a continuous variable. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.
suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)
df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)
groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]
bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]
models.b <- mclapply(groupvars, function(this_groupvar){
models.bcells <- lapply(bcells, function(this_bcell){
this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar,
" * Time + (1|PatientID)"))
suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
this_anova <- Anova(this_model, type = 2)
this_pvalues <- this_anova$`Pr(>Chisq)`
names(this_pvalues) <- rownames(this_anova)
#return(this_pvalues)
return(list(model = this_model,
pvals = this_pvalues))
})
names(models.bcells) <- colnames(df)[bcellcyto]
pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
rownames(pvalues) <- bcells
#return(pvalues)
return(list(model = models.bcells,
pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1,3)]))
# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix),
# method = "BH"),
# nrow = nrow(pvals.matrix),
# ncol = ncol(pvals.matrix),
# byrow = F)
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]
# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]
pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]
kable(shortlist,
digits = 3,
row.names = T,
caption = "Significant results") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 10) %>%
scroll_box(width = "100%")
Significant results
|
|
B-cell
|
parameter
|
qvalue
|
|
885
|
CD27+IgD+ unswitched memory
|
Outcome:Time
|
0.000
|
|
893
|
CD19+CD268+
|
Outcome:Time
|
0.000
|
|
297
|
CD19+27+IgD-38++IgG ASC
|
LowIntermacs
|
0.033
|
|
790
|
IP-10
|
VAD.Indication:Time
|
0.035
|
|
69
|
num lymph
|
AgeGreater60:Time
|
0.038
|
|
514
|
IL-6
|
RVAD:Time
|
0.053
|
|
2
|
num lymph
|
AgeGreater60
|
0.060
|
|
765
|
CD19+CD5+CD11b+
|
VAD.Indication:Time
|
0.069
|
|
770
|
TNF-a
|
VAD.Indication:Time
|
0.077
|
|
858
|
MCP-1
|
Outcome
|
0.192
|
|
301
|
TNF-a
|
LowIntermacs
|
0.193
|
|
219
|
CD19+24dim38dim naive mature
|
Sex:Time
|
0.208
|
|
49
|
IL-8
|
AgeGreater60
|
0.229
|
|
137
|
lymph
|
Sex
|
0.233
|
|
46
|
IL-15
|
AgeGreater60
|
0.237
|
|
896
|
CD19+CD5+
|
Outcome:Time
|
0.264
|
|
383
|
IFN-a2
|
LowIntermacs:Time
|
0.267
|
|
899
|
CD19+CD5+CD11b+
|
Outcome:Time
|
0.280
|
|
762
|
CD19+CD5+
|
VAD.Indication:Time
|
0.289
|
|
345
|
CD27-38++ transitional
|
LowIntermacs:Time
|
0.298
|
|
97
|
IL-12(p40)
|
AgeGreater60:Time
|
0.300
|
|
761
|
CD19+CD11b+
|
VAD.Indication:Time
|
0.321
|
|
992
|
MCP-1
|
Survival
|
0.324
|
|
904
|
TNF-a
|
Outcome:Time
|
0.340
|
|
3
|
lymph
|
AgeGreater60
|
0.354
|
|
818
|
CD27+IgD+ unswitched memory
|
Outcome
|
0.356
|
|
764
|
CD19+CD5+CD24hi
|
VAD.Indication:Time
|
0.362
|
|
887
|
CD27+IgD+IgM+ nonswitched memory
|
Outcome:Time
|
0.363
|
|
1050
|
IL-6
|
Survival:Time
|
0.367
|
|
268
|
sCD40L
|
Sex:Time
|
0.369
|
|
91
|
CD19+CD11b+
|
AgeGreater60:Time
|
0.384
|
|
123
|
Fractalkine
|
AgeGreater60:Time
|
0.394
|
|
377
|
IL-1b
|
LowIntermacs:Time
|
0.409
|
|
1049
|
IL-3
|
Survival:Time
|
0.415
|
|
115
|
IFN-a2
|
AgeGreater60:Time
|
0.416
|
|
791
|
MCP-1
|
VAD.Indication:Time
|
0.437
|
|
68
|
num Total PBMC
|
AgeGreater60:Time
|
0.441
|
|
380
|
IL-6
|
LowIntermacs:Time
|
0.464
|
|
731
|
G-CSF
|
VAD.Indication
|
0.465
|
|
77
|
CD27-38++ transitional
|
AgeGreater60:Time
|
0.466
|
|
1010
|
CD3 of live lymph
|
Survival:Time
|
0.475
|
|
150
|
CD27+IgD+IgM+ nonswitched memory
|
Sex
|
0.476
|
|
518
|
IL-8
|
RVAD:Time
|
0.476
|
|
898
|
CD19+CD5+CD24hi
|
Outcome:Time
|
0.479
|
|
252
|
Eotaxin
|
Sex:Time
|
0.480
|
|
523
|
MCP-1
|
RVAD:Time
|
0.487
|
|
382
|
TGF-a
|
LowIntermacs:Time
|
0.489
|
|
206
|
CD3 of live lymph
|
Sex:Time
|
0.492
|
|
33
|
TNF-a
|
AgeGreater60
|
0.493
|
|
234
|
TNF-a
|
Sex:Time
|
0.493
|
|
783
|
IL-15
|
VAD.Indication:Time
|
0.494
|
|
211
|
CD27-38++ transitional
|
Sex:Time
|
0.495
|
|
817
|
CD27-IgD- switched memory
|
Outcome
|
0.496
|
|
416
|
CD27+IgD+ unswitched memory
|
RVAD
|
0.496
|
|
794
|
MIP-1a
|
VAD.Indication:Time
|
0.505
|
|
51
|
Eotaxin
|
AgeGreater60
|
0.508
|
|
924
|
IP-10
|
Outcome:Time
|
0.508
|
|
271
|
lymph
|
LowIntermacs
|
0.513
|
|
95
|
CD19+CD5+CD11b+
|
AgeGreater60:Time
|
0.522
|
|
126
|
GM-CSF
|
AgeGreater60:Time
|
0.527
|
|
128
|
G-CSF
|
AgeGreater60:Time
|
0.529
|
|
20
|
CD19CD24hiCD38-memory
|
AgeGreater60
|
0.529
|
|
29
|
CD19+27+IgD-38++IgG ASC
|
AgeGreater60
|
0.531
|
|
895
|
CD19+CD11b+
|
Outcome:Time
|
0.531
|
|
746
|
CD27+38++plasma blasts
|
VAD.Indication:Time
|
0.531
|
|
185
|
Eotaxin
|
Sex
|
0.532
|
|
820
|
CD27+IgD+IgM+ nonswitched memory
|
Outcome
|
0.533
|
|
160
|
CD19+CD27+CD24hi
|
Sex
|
0.533
|
|
391
|
Fractalkine
|
LowIntermacs:Time
|
0.535
|
|
739
|
num lymph
|
VAD.Indication:Time
|
0.536
|
|
371
|
IL-5
|
LowIntermacs:Time
|
0.538
|
|
90
|
CD268 of +27-38++transitional
|
AgeGreater60:Time
|
0.539
|
|
183
|
IL-8
|
Sex
|
0.540
|
|
664
|
G-CSF
|
Sensitized:Time
|
0.540
|
|
1027
|
CD19+CD268+
|
Survival:Time
|
0.540
|
|
516
|
TGF-a
|
RVAD:Time
|
0.545
|
|
386
|
Eotaxin
|
LowIntermacs:Time
|
0.545
|
We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.
require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
this_group <-siggroups[ii]
this_bcell <- as.character(shortlist$`B-cell`[ii])
cat(" \n###", as.character(shortlist$`B-cell`[ii]), "\n")
sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
)
cat(" \n")
suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
cat(" \n")
}
CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:Time
|
-2.667973
|
0.494540
|
62.02792
|
-5.394858
|
0.0000011
|
|
OutcomeDied post OHT
|
23.798615
|
6.761184
|
17.90907
|
3.519888
|
0.0024617
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:Time
|
-6.8213719
|
1.5139567
|
62.00943
|
-4.505659
|
0.0000299
|
|
Time
|
-1.2473123
|
0.3522343
|
62.49855
|
-3.541144
|
0.0007592
|
|
OutcomeAlive s/p OHT:Time
|
0.9383259
|
0.4056434
|
62.45638
|
2.313179
|
0.0240193
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh
|
3.058696
|
0.774233
|
45.35361
|
3.950614
|
0.0002697
|

IP-10
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
-126.82887
|
33.85589
|
54.58765
|
-3.746139
|
0.0004345
|
|
Time
|
60.64459
|
17.34161
|
54.85011
|
3.497056
|
0.0009404
|

num lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
8206.115
|
1791.316
|
1287.688
|
4.581054
|
0.0000051
|
|
AgeGreater60older:Time
|
-8800.327
|
2381.169
|
2274.895
|
-3.695802
|
0.0002244
|

IL-6
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:Time
|
32.60694
|
9.175249
|
54.21954
|
3.553793
|
0.0007958
|

num lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
8206.115
|
1791.316
|
1287.688
|
4.581054
|
0.0000051
|
|
AgeGreater60older:Time
|
-8800.327
|
2381.169
|
2274.895
|
-3.695802
|
0.0002244
|

CD19+CD5+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
0.7535101
|
0.2208725
|
66.53183
|
3.411516
|
0.0011029
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
-5.629434
|
1.680142
|
55.89628
|
-3.350570
|
0.0014504
|
|
VAD.IndicationDT
|
23.272245
|
10.340359
|
28.83495
|
2.250622
|
0.0322084
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh
|
23.97608
|
8.347092
|
41.27354
|
2.872387
|
0.0064058
|

CD19+24dim38dim naive mature
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
-0.8161430
|
0.2628953
|
64.83725
|
-3.104441
|
0.0028253
|
|
SexMale:Time
|
0.9045098
|
0.3076620
|
64.86966
|
2.939946
|
0.0045445
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
-17.61531
|
7.156886
|
25.50525
|
-2.46131
|
0.0209393
|

IL-15
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
7.790226
|
2.627262
|
23.91797
|
2.96515
|
0.0067556
|

CD19+CD5+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.8658199
|
0.2549408
|
64.48772
|
3.396161
|
0.0011744
|
|
OutcomeDied post OHT:Time
|
-3.3958199
|
1.1020936
|
62.63883
|
-3.081244
|
0.0030615
|
|
OutcomeAlive s/p OHT:Time
|
-0.6785876
|
0.2937450
|
64.32566
|
-2.310125
|
0.0241003
|

IFN-a2
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
7.094916
|
2.400303
|
55.17787
|
2.955842
|
0.0045797
|
|
LowIntermacsHigh:Time
|
-8.250956
|
2.967875
|
54.85053
|
-2.780089
|
0.0074308
|

CD19+CD5+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.9291898
|
0.2104973
|
64.10073
|
4.414260
|
0.0000397
|
|
OutcomeAlive s/p OHT:Time
|
-0.8534246
|
0.2425036
|
63.97151
|
-3.519225
|
0.0008029
|

CD19+CD5+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
0.7495301
|
0.2761314
|
67.17207
|
2.714397
|
0.0084299
|

CD27-38++ transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.2728189
|
0.0943080
|
68.33055
|
2.892849
|
0.0051174
|
|
LowIntermacsHigh:Time
|
-0.3029055
|
0.1126997
|
67.21515
|
-2.687721
|
0.0090598
|

IL-12(p40)
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
4.488736
|
1.571299
|
56.15354
|
2.856703
|
0.0059916
|
|
AgeGreater60older:Time
|
-4.915360
|
1.833222
|
55.84263
|
-2.681268
|
0.0096251
|

CD19+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
0.5911116
|
0.2251629
|
65.39615
|
2.625262
|
0.0107693
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Survivaldead
|
500.4575
|
220.1017
|
30.55848
|
2.273755
|
0.0301634
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
-5.527423
|
1.705528
|
53.73771
|
-3.240887
|
0.0020479
|
|
OutcomeAlive s/p OHT:Time
|
6.230423
|
2.030162
|
53.98616
|
3.068929
|
0.0033572
|
|
OutcomeDied:Time
|
6.757500
|
2.229274
|
53.60892
|
3.031256
|
0.0037457
|
|
OutcomeDied
|
-32.035464
|
13.772117
|
24.37281
|
-2.326110
|
0.0286425
|
|
OutcomeAlive s/p OHT
|
-28.339350
|
12.411581
|
25.62297
|
-2.283299
|
0.0309577
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.7428399
|
0.3610038
|
69.66204
|
2.057706
|
0.0433625
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:Time
|
-2.667973
|
0.494540
|
62.02792
|
-5.394858
|
0.0000011
|
|
OutcomeDied post OHT
|
23.798615
|
6.761184
|
17.90907
|
3.519888
|
0.0024617
|

CD19+CD5+CD24hi
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
0.4514841
|
0.1801168
|
68.30031
|
2.506618
|
0.0145744
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:Time
|
-4.960664
|
1.778346
|
62.05844
|
-2.789481
|
0.0070021
|
|
OutcomeDied post OHT
|
42.763036
|
16.441772
|
20.60956
|
2.600878
|
0.0168391
|

IL-6
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Survivaldead:Time
|
20.49333
|
8.218759
|
54.28222
|
2.493482
|
0.0157287
|

sCD40L
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
564.2348
|
192.6758
|
56.44318
|
2.928415
|
0.0049057
|
|
SexMale:Time
|
-561.8438
|
225.8697
|
56.13655
|
-2.487468
|
0.0158624
|

CD19+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.5499941
|
0.1571744
|
65.94446
|
3.499260
|
0.0008412
|
|
AgeGreater60older:Time
|
-0.5051776
|
0.2059164
|
65.50984
|
-2.453314
|
0.0168239
|

Fractalkine
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
12.02533
|
4.519847
|
54.65411
|
2.660561
|
0.0102192
|
|
AgeGreater60older:Time
|
-12.81993
|
5.273092
|
54.32486
|
-2.431197
|
0.0183777
|

IL-1b
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.8271702
|
0.2556659
|
53.88754
|
3.235356
|
0.0020785
|
|
LowIntermacsHigh:Time
|
-0.7446451
|
0.3108987
|
53.73054
|
-2.395137
|
0.0201302
|

IL-3
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.3819202
|
0.1262965
|
56.47376
|
3.023997
|
0.0037486
|
|
Survivaldead:Time
|
-0.5153221
|
0.2164933
|
55.43845
|
-2.380315
|
0.0207621
|

IFN-a2
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
7.368396
|
2.774815
|
55.86789
|
2.655454
|
0.0103010
|
|
AgeGreater60older:Time
|
-7.704730
|
3.239224
|
55.41990
|
-2.378573
|
0.0208522
|
|
AgeGreater60older
|
44.726614
|
21.390894
|
24.55143
|
2.090918
|
0.0470524
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT
|
680.47441
|
249.43832
|
27.52168
|
2.728027
|
0.0109658
|
|
VAD.IndicationDT:Time
|
-91.32862
|
39.28639
|
57.36259
|
-2.324688
|
0.0236507
|

num Total PBMC
IL-6
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh:Time
|
-18.54127
|
8.228838
|
55.84312
|
-2.253206
|
0.0281925
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD27-38++ transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.1924463
|
0.0792502
|
69.27155
|
2.428340
|
0.0177687
|
|
AgeGreater60older:Time
|
-0.2343863
|
0.1043092
|
67.73029
|
-2.247034
|
0.0279003
|

CD3 of live lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Survivaldead:Time
|
-1.829251
|
0.8242817
|
66.33046
|
-2.219206
|
0.0298978
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
-20.7260608
|
7.6448089
|
21.84154
|
-2.711129
|
0.0128054
|
|
Time
|
-0.9006538
|
0.3895893
|
66.29166
|
-2.311803
|
0.0239044
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:Time
|
10.23949
|
4.619034
|
54.36592
|
2.216804
|
0.030839
|

CD19+CD5+CD24hi
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.5935719
|
0.1694987
|
65.72053
|
3.501927
|
0.0008357
|
|
OutcomeAlive s/p OHT:Time
|
-0.5739728
|
0.1953535
|
65.49771
|
-2.938124
|
0.0045549
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
128.54981
|
47.668530
|
18.34905
|
2.696744
|
0.0145850
|
|
Time
|
11.40686
|
4.560498
|
55.36903
|
2.501231
|
0.0153620
|
|
SexMale:Time
|
-11.77662
|
5.342803
|
55.18872
|
-2.204202
|
0.0316985
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:Time
|
93.61331
|
42.84101
|
54.30507
|
2.185133
|
0.0332124
|

TGF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh:Time
|
-1.1233582
|
0.5157778
|
54.51579
|
-2.177989
|
0.0337513
|
|
Time
|
0.8378511
|
0.4175992
|
54.60020
|
2.006352
|
0.0497822
|

CD3 of live lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale:Time
|
-1.143545
|
0.5277063
|
66.36504
|
-2.167011
|
0.0338279
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale:Time
|
3.767525
|
1.740926
|
57.15748
|
2.164093
|
0.0346496
|
|
Time
|
-3.158273
|
1.483377
|
57.67143
|
-2.129110
|
0.0375269
|

IL-15
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.4821335
|
0.1913209
|
54.97323
|
2.520026
|
0.0146688
|
|
VAD.IndicationDT:Time
|
-0.8073232
|
0.3733218
|
54.80952
|
-2.162540
|
0.0349572
|

CD27-38++ transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.2418057
|
0.0994538
|
65.99623
|
2.431336
|
0.0177663
|
|
SexMale:Time
|
-0.2512294
|
0.1163638
|
66.05082
|
-2.159000
|
0.0344873
|

CD27-IgD- switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes
|
8.138537
|
3.426992
|
21.4197
|
2.374834
|
0.0269627
|

MIP-1a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
-2.726267
|
1.282933
|
55.03723
|
-2.125027
|
0.038089
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
118.1149
|
47.98599
|
19.32249
|
2.461445
|
0.0234005
|

IP-10
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeAlive s/p OHT:Time
|
114.5049
|
42.55788
|
53.61146
|
2.690569
|
0.009491
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+CD5+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.5382593
|
0.1602283
|
67.49846
|
3.359327
|
0.0012883
|
|
AgeGreater60older:Time
|
-0.4334330
|
0.2101801
|
66.78306
|
-2.062198
|
0.0430827
|

GM-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
34.128884
|
14.361503
|
20.92304
|
2.376415
|
0.0271092
|
|
AgeGreater60older:Time
|
-4.022234
|
1.970956
|
54.45762
|
-2.040753
|
0.0461351
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
114.60139
|
42.096120
|
38.21983
|
2.722374
|
0.0097107
|
|
AgeGreater60older:Time
|
-16.24051
|
7.977444
|
57.78234
|
-2.035804
|
0.0463639
|

CD19CD24hiCD38-memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
2.044292
|
0.8970807
|
39.13156
|
2.278828
|
0.0282137
|

CD19+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.7895884
|
0.2140589
|
63.20206
|
3.688650
|
0.0004710
|
|
OutcomeAlive s/p OHT:Time
|
-0.6983342
|
0.2465523
|
63.12486
|
-2.832397
|
0.0061934
|

CD27+38++plasma blasts
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
0.1018402
|
0.0463711
|
65.98620
|
2.196200
|
0.0315970
|
|
VAD.IndicationDT:Time
|
-0.1781890
|
0.0880343
|
66.32796
|
-2.024086
|
0.0469921
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
128.54981
|
47.668530
|
18.34905
|
2.696744
|
0.0145850
|
|
Time
|
11.40686
|
4.560498
|
55.36903
|
2.501231
|
0.0153620
|
|
SexMale:Time
|
-11.77662
|
5.342803
|
55.18872
|
-2.204202
|
0.0316985
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:Time
|
-4.960664
|
1.778346
|
62.05844
|
-2.789481
|
0.0070021
|
|
OutcomeDied post OHT
|
42.763036
|
16.441772
|
20.60956
|
2.600878
|
0.0168391
|

CD19+CD27+CD24hi
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

Fractalkine
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
9.181153
|
4.034219
|
54.31416
|
2.275819
|
0.0268244
|
|
LowIntermacsHigh:Time
|
-10.023051
|
4.986464
|
54.05128
|
-2.010052
|
0.0494276
|

num lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:Time
|
5487.098
|
2740.267
|
7534.727
|
2.002396
|
0.045278
|

IL-5
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD268 of +27-38++transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
-0.5331745
|
0.2039893
|
64.53309
|
-2.613738
|
0.0111329
|

TGF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Time
|
9.357596
|
4.075572
|
55.21464
|
2.29602
|
0.0254948
|

Two-way repeated measures anova
We analyzed the differences in B-cell levels for various features using a two-way repeated measures ANOVA. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.
suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)
df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)
groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]
bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]
models.b <- mclapply(groupvars, function(this_groupvar){
models.bcells <- lapply(bcells, function(this_bcell){
this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar,
" * factor(Time) + (1|PatientID)"))
suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
this_anova <- Anova(this_model, type = 2)
this_pvalues <- this_anova$`Pr(>Chisq)`
names(this_pvalues) <- rownames(this_anova)
#return(this_pvalues)
return(list(model = this_model,
pvals = this_pvalues))
})
names(models.bcells) <- colnames(df)[bcellcyto]
pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
rownames(pvalues) <- bcells
#return(pvalues)
return(list(model = models.bcells,
pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1,3)]))
# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix),
# method = "BH"),
# nrow = nrow(pvals.matrix),
# ncol = ncol(pvals.matrix),
# byrow = F)
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]
# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]
pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]
kable(shortlist,
digits = 3,
row.names = T,
caption = "Significant results") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
font_size = 10) %>%
scroll_box(width = "100%")
Significant results
|
|
B-cell
|
parameter
|
qvalue
|
|
791
|
MCP-1
|
VAD.Indication:factor(Time)
|
0.000
|
|
893
|
CD19+CD268+
|
Outcome:factor(Time)
|
0.000
|
|
885
|
CD27+IgD+ unswitched memory
|
Outcome:factor(Time)
|
0.003
|
|
530
|
G-CSF
|
RVAD:factor(Time)
|
0.022
|
|
297
|
CD19+27+IgD-38++IgG ASC
|
LowIntermacs
|
0.026
|
|
790
|
IP-10
|
VAD.Indication:factor(Time)
|
0.027
|
|
2
|
num lymph
|
AgeGreater60
|
0.030
|
|
90
|
CD268 of +27-38++transitional
|
AgeGreater60:factor(Time)
|
0.073
|
|
618
|
CD27+IgD-IgM+ switched memory
|
Sensitized:factor(Time)
|
0.088
|
|
770
|
TNF-a
|
VAD.Indication:factor(Time)
|
0.091
|
|
858
|
MCP-1
|
Outcome
|
0.183
|
|
514
|
IL-6
|
RVAD:factor(Time)
|
0.209
|
|
137
|
lymph
|
Sex
|
0.227
|
|
766
|
CD19+27+IgD-38++IgG ASC
|
VAD.Indication:factor(Time)
|
0.244
|
|
301
|
TNF-a
|
LowIntermacs
|
0.253
|
|
498
|
CD19+27+IgD-38++IgG ASC
|
RVAD:factor(Time)
|
0.266
|
|
46
|
IL-15
|
AgeGreater60
|
0.268
|
|
49
|
IL-8
|
AgeGreater60
|
0.295
|
|
345
|
CD27-38++ transitional
|
LowIntermacs:factor(Time)
|
0.296
|
|
3
|
lymph
|
AgeGreater60
|
0.300
|
|
518
|
IL-8
|
RVAD:factor(Time)
|
0.307
|
|
883
|
CD27+IgD- switched memory
|
Outcome:factor(Time)
|
0.350
|
|
992
|
MCP-1
|
Survival
|
0.416
|
|
383
|
IFN-a2
|
LowIntermacs:factor(Time)
|
0.420
|
|
765
|
CD19+CD5+CD11b+
|
VAD.Indication:factor(Time)
|
0.424
|
|
69
|
num lymph
|
AgeGreater60:factor(Time)
|
0.426
|
|
215
|
CD27+IgD+ unswitched memory
|
Sex:factor(Time)
|
0.466
|
|
818
|
CD27+IgD+ unswitched memory
|
Outcome
|
0.469
|
|
78
|
CD27-IgD+ mature naive
|
AgeGreater60:factor(Time)
|
0.487
|
|
879
|
CD19+CD27+
|
Outcome:factor(Time)
|
0.527
|
|
128
|
G-CSF
|
AgeGreater60:factor(Time)
|
0.566
|
|
878
|
CD19+CD27-
|
Outcome:factor(Time)
|
0.571
|
|
271
|
lymph
|
LowIntermacs
|
0.578
|
|
371
|
IL-5
|
LowIntermacs:factor(Time)
|
0.585
|
|
526
|
MIP-1a
|
RVAD:factor(Time)
|
0.596
|
|
817
|
CD27-IgD- switched memory
|
Outcome
|
0.626
|
|
900
|
CD19+27+IgD-38++IgG ASC
|
Outcome:factor(Time)
|
0.634
|
|
739
|
num lymph
|
VAD.Indication:factor(Time)
|
0.639
|
|
97
|
IL-12(p40)
|
AgeGreater60:factor(Time)
|
0.653
|
|
886
|
CD27+IgD-IgM+ switched memory
|
Outcome:factor(Time)
|
0.658
|
|
150
|
CD27+IgD+IgM+ nonswitched memory
|
Sex
|
0.662
|
|
483
|
CD27+IgD+ unswitched memory
|
RVAD:factor(Time)
|
0.667
|
|
217
|
CD27+IgD+IgM+ nonswitched memory
|
Sex:factor(Time)
|
0.677
|
|
115
|
IFN-a2
|
AgeGreater60:factor(Time)
|
0.682
|
|
491
|
CD19+CD268+
|
RVAD:factor(Time)
|
0.688
|
|
33
|
TNF-a
|
AgeGreater60
|
0.706
|
|
1027
|
CD19+CD268+
|
Survival:factor(Time)
|
0.717
|
|
762
|
CD19+CD5+
|
VAD.Indication:factor(Time)
|
0.719
|
|
377
|
IL-1b
|
LowIntermacs:factor(Time)
|
0.719
|
|
731
|
G-CSF
|
VAD.Indication
|
0.724
|
|
51
|
Eotaxin
|
AgeGreater60
|
0.725
|
|
820
|
CD27+IgD+IgM+ nonswitched memory
|
Outcome
|
0.730
|
|
904
|
TNF-a
|
Outcome:factor(Time)
|
0.733
|
|
183
|
IL-8
|
Sex
|
0.743
|
|
490
|
CD19+27-38+CD5+transitionals
|
RVAD:factor(Time)
|
0.745
|
|
89
|
CD19+CD268+
|
AgeGreater60:factor(Time)
|
0.745
|
|
416
|
CD27+IgD+ unswitched memory
|
RVAD
|
0.748
|
|
185
|
Eotaxin
|
Sex
|
0.755
|
|
1062
|
MIP-1a
|
Survival:factor(Time)
|
0.764
|
|
29
|
CD19+27+IgD-38++IgG ASC
|
AgeGreater60
|
0.765
|
|
234
|
TNF-a
|
Sex:factor(Time)
|
0.766
|
|
20
|
CD19CD24hiCD38-memory
|
AgeGreater60
|
0.769
|
We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.
require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
this_group <-siggroups[ii]
this_bcell <- as.character(shortlist$`B-cell`[ii])
cat(" \n###", as.character(shortlist$`B-cell`[ii]), "\n")
sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
)
cat(" \n")
suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
cat(" \n")
}
MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:factor(Time)1
|
1396.314
|
299.8357
|
49.76712
|
4.656931
|
2.42e-05
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:factor(Time)3
|
-58.61538
|
14.204316
|
46.18057
|
-4.126590
|
0.0001523
|
|
OutcomeDied post OHT:factor(Time)8
|
-55.76061
|
13.538518
|
46.05387
|
-4.118664
|
0.0001567
|
|
factor(Time)21
|
-27.32462
|
8.270658
|
46.63558
|
-3.303802
|
0.0018378
|
|
OutcomeAlive s/p OHT:factor(Time)21
|
27.40774
|
9.419763
|
46.53642
|
2.909600
|
0.0055342
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:factor(Time)8
|
-27.59770
|
4.932075
|
46.11295
|
-5.595555
|
0.0000012
|
|
OutcomeDied post OHT
|
27.31810
|
7.270274
|
22.64763
|
3.757506
|
0.0010464
|
|
OutcomeDied post OHT:factor(Time)1
|
-13.37738
|
5.047487
|
45.94619
|
-2.650306
|
0.0109931
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)1
|
274.3137
|
66.34657
|
49.26911
|
4.134557
|
0.0001379
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh
|
3.095
|
1.279721
|
68.99999
|
2.418497
|
0.0182277
|

IP-10
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:factor(Time)5
|
-999.2242
|
294.0807
|
48.43045
|
-3.397789
|
0.0013666
|
|
factor(Time)5
|
463.4742
|
156.2366
|
49.13082
|
2.966490
|
0.0046413
|
|
VAD.IndicationDT:factor(Time)8
|
-806.2164
|
291.6175
|
48.29663
|
-2.764637
|
0.0080454
|
|
VAD.IndicationDT:factor(Time)3
|
-723.7126
|
291.6175
|
48.29663
|
-2.481719
|
0.0166053
|
|
factor(Time)8
|
335.4664
|
151.5492
|
48.68098
|
2.213581
|
0.0315752
|

num lymph
CD268 of +27-38++transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older:factor(Time)8
|
-27.22177
|
10.33554
|
54.58102
|
-2.633803
|
0.0109632
|
|
factor(Time)14
|
-24.92941
|
12.43679
|
55.44562
|
-2.004489
|
0.0499111
|

CD27+IgD-IgM+ switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SensitizedYes:factor(Time)3
|
17.965877
|
5.302290
|
23.31979
|
3.388324
|
0.0024949
|
|
factor(Time)5
|
9.747852
|
4.547781
|
23.30374
|
2.143431
|
0.0427391
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:factor(Time)3
|
-52.08619
|
14.55496
|
49.73388
|
-3.578586
|
0.0007821
|
|
VAD.IndicationDT:factor(Time)8
|
-51.15105
|
14.55496
|
49.73388
|
-3.514337
|
0.0009497
|
|
VAD.IndicationDT:factor(Time)5
|
-48.24997
|
14.67386
|
49.90668
|
-3.288159
|
0.0018522
|
|
VAD.IndicationDT
|
39.41501
|
12.48552
|
46.96050
|
3.156857
|
0.0027848
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IL-6
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)8
|
303.4749
|
81.51192
|
47.97526
|
3.723074
|
0.0005171
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
-22.22807
|
9.001586
|
53.16948
|
-2.46935
|
0.0167811
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)5
|
4.722429
|
1.330913
|
60.14684
|
3.548263
|
0.0007592
|
|
VAD.IndicationDT:factor(Time)5
|
-6.577445
|
1.874482
|
57.09610
|
-3.508940
|
0.0008850
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh
|
29.20377
|
11.74819
|
61.39306
|
2.48581
|
0.015663
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)5
|
11.55470
|
2.912778
|
59.01745
|
3.966900
|
0.0001997
|
|
factor(Time)14
|
-2.17979
|
1.043488
|
56.60859
|
-2.088945
|
0.0412201
|

IL-15
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older
|
6.953102
|
3.180869
|
41.10986
|
2.185913
|
0.034572
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD27-38++ transitional
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh:factor(Time)21
|
-11.380113
|
2.894440
|
59.65375
|
-3.931715
|
0.0002222
|
|
factor(Time)21
|
9.440964
|
2.450218
|
61.33981
|
3.853112
|
0.0002815
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)8
|
121.59744
|
36.36762
|
48.07180
|
3.343564
|
0.0016092
|
|
factor(Time)1
|
37.88158
|
16.80689
|
49.14302
|
2.253931
|
0.0286938
|

CD27+IgD- switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)14
|
16.67333
|
5.808017
|
46.36242
|
2.870744
|
0.0061524
|
|
OutcomeDied:factor(Time)14
|
-23.76894
|
9.569855
|
46.52627
|
-2.483731
|
0.0166625
|
|
OutcomeDied:factor(Time)3
|
-20.00222
|
8.099103
|
47.20672
|
-2.469683
|
0.0171942
|
|
factor(Time)21
|
14.08234
|
6.267358
|
47.25499
|
2.246934
|
0.0293537
|
|
OutcomeDied:factor(Time)8
|
-16.05135
|
7.162484
|
46.77082
|
-2.241032
|
0.0298109
|
|
OutcomeDied:factor(Time)5
|
-15.19473
|
6.853678
|
46.39420
|
-2.217018
|
0.0315609
|
|
OutcomeAlive s/p OHT:factor(Time)14
|
-14.36929
|
6.997197
|
46.40850
|
-2.053579
|
0.0456758
|

MCP-1
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IFN-a2
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh:factor(Time)5
|
-83.38244
|
25.81665
|
48.86115
|
-3.229793
|
0.0022176
|
|
LowIntermacsHigh:factor(Time)8
|
-74.03854
|
26.08098
|
49.05338
|
-2.838794
|
0.0065708
|
|
LowIntermacsHigh:factor(Time)3
|
-71.00660
|
26.08098
|
49.05338
|
-2.722543
|
0.0089451
|
|
LowIntermacsHigh
|
64.16333
|
23.82257
|
40.13825
|
2.693384
|
0.0102726
|
|
factor(Time)8
|
53.76254
|
21.43059
|
49.46030
|
2.508682
|
0.0154438
|
|
factor(Time)5
|
42.82700
|
20.30165
|
48.76539
|
2.109532
|
0.0400558
|

CD19+CD5+CD11b+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:factor(Time)21
|
18.60843
|
5.699112
|
56.68510
|
3.265145
|
0.0018584
|
|
VAD.IndicationDT:factor(Time)8
|
10.32734
|
4.156816
|
55.37079
|
2.484436
|
0.0160275
|

num lymph
CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale:factor(Time)8
|
9.848232
|
2.746704
|
54.41204
|
3.585472
|
0.0007200
|
|
factor(Time)8
|
-8.159681
|
2.352215
|
54.37376
|
-3.468936
|
0.0010300
|
|
factor(Time)14
|
-8.101354
|
3.062814
|
55.12449
|
-2.645069
|
0.0106199
|
|
SexMale
|
-10.353852
|
3.922084
|
26.14988
|
-2.639885
|
0.0138005
|
|
factor(Time)5
|
-7.781354
|
3.062814
|
55.12449
|
-2.540590
|
0.0139123
|
|
SexMale:factor(Time)5
|
8.682914
|
3.494502
|
55.06965
|
2.484736
|
0.0160327
|
|
SexMale:factor(Time)3
|
5.898797
|
2.849382
|
54.68689
|
2.070202
|
0.0431688
|
|
SexMale:factor(Time)14
|
7.448953
|
3.625289
|
54.98976
|
2.054720
|
0.0446698
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:factor(Time)8
|
-27.59770
|
4.932075
|
46.11295
|
-5.595555
|
0.0000012
|
|
OutcomeDied post OHT
|
27.31810
|
7.270274
|
22.64763
|
3.757506
|
0.0010464
|
|
OutcomeDied post OHT:factor(Time)1
|
-13.37738
|
5.047487
|
45.94619
|
-2.650306
|
0.0109931
|

CD27-IgD+ mature naive
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)14
|
-19.34727
|
6.403688
|
54.77962
|
-3.021270
|
0.0038210
|
|
AgeGreater60older:factor(Time)14
|
17.50253
|
7.722534
|
54.66856
|
2.266424
|
0.0274049
|

CD19+CD27+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:factor(Time)8
|
-30.68974
|
11.896519
|
46.35300
|
-2.579725
|
0.0131220
|
|
OutcomeDied:factor(Time)8
|
-18.18883
|
8.283555
|
46.91757
|
-2.195776
|
0.0330881
|
|
OutcomeDied:factor(Time)3
|
-19.62377
|
9.360583
|
47.47076
|
-2.096426
|
0.0413996
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+CD27-
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT:factor(Time)8
|
30.68584
|
11.990106
|
46.36468
|
2.559264
|
0.0138150
|
|
OutcomeDied:factor(Time)8
|
18.19237
|
8.347928
|
46.94556
|
2.179267
|
0.0343639
|
|
OutcomeDied:factor(Time)3
|
19.65350
|
9.432447
|
47.51498
|
2.083606
|
0.0425996
|

lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

IL-5
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
LowIntermacsHigh
|
14.33625
|
4.540629
|
50.69022
|
3.157327
|
0.0026812
|
|
LowIntermacsHigh:factor(Time)5
|
-16.35500
|
5.441291
|
49.36905
|
-3.005720
|
0.0041567
|
|
LowIntermacsHigh:factor(Time)3
|
-15.72446
|
5.494185
|
49.66232
|
-2.862019
|
0.0061466
|
|
LowIntermacsHigh:factor(Time)8
|
-13.08094
|
5.494185
|
49.66232
|
-2.380870
|
0.0211494
|
|
LowIntermacsHigh:factor(Time)1
|
-12.48229
|
5.441291
|
49.36905
|
-2.293994
|
0.0260840
|

MIP-1a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)3
|
41.28308
|
12.390641
|
48.60325
|
3.331795
|
0.0016549
|
|
factor(Time)3
|
-11.46708
|
5.569941
|
49.22844
|
-2.058743
|
0.0448305
|

CD27-IgD- switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied:factor(Time)8
|
19.81291
|
9.256631
|
50.10634
|
2.140402
|
0.0372133
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied:factor(Time)5
|
7.770913
|
2.375060
|
48.89002
|
3.271880
|
0.0019634
|
|
OutcomeAlive s/p OHT:factor(Time)5
|
6.157210
|
2.440892
|
50.31147
|
2.522524
|
0.0148592
|
|
factor(Time)14
|
-4.926591
|
2.013314
|
49.03199
|
-2.447005
|
0.0180351
|
|
factor(Time)5
|
-3.476482
|
1.679134
|
48.86411
|
-2.070402
|
0.0437202
|

num lymph
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:factor(Time)21
|
218165.6
|
69573.59
|
5501.95
|
3.135753
|
0.0017232
|

IL-12(p40)
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older:factor(Time)3
|
-39.92823
|
16.10410
|
49.75241
|
-2.479383
|
0.0165940
|
|
AgeGreater60older:factor(Time)8
|
-39.09737
|
16.10410
|
49.75241
|
-2.427790
|
0.0188521
|
|
AgeGreater60older:factor(Time)5
|
-36.10684
|
15.62485
|
49.43068
|
-2.310860
|
0.0250524
|
|
factor(Time)8
|
29.12464
|
13.88649
|
50.03739
|
2.097336
|
0.0410371
|

CD27+IgD-IgM+ switched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)14
|
24.15202
|
6.766597
|
47.02757
|
3.569301
|
0.0008372
|
|
factor(Time)5
|
17.32212
|
5.643461
|
47.08561
|
3.069415
|
0.0035536
|
|
OutcomeAlive s/p OHT:factor(Time)14
|
-21.39064
|
8.148405
|
47.15503
|
-2.625132
|
0.0116397
|
|
OutcomeDied:factor(Time)14
|
-24.01191
|
11.131654
|
47.47898
|
-2.157084
|
0.0360919
|
|
OutcomeDied:factor(Time)5
|
-16.38868
|
7.982839
|
47.08336
|
-2.052989
|
0.0456533
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
-31.50752
|
9.007301
|
37.48747
|
-3.497997
|
0.0012251
|
|
SexMale:factor(Time)8
|
27.02760
|
8.735159
|
54.73131
|
3.094116
|
0.0031071
|
|
SexMale:factor(Time)3
|
24.44017
|
9.046112
|
55.34926
|
2.701732
|
0.0091385
|
|
factor(Time)8
|
-20.00903
|
7.482252
|
54.65772
|
-2.674199
|
0.0098594
|
|
factor(Time)3
|
-15.59653
|
7.482252
|
54.65772
|
-2.084470
|
0.0418040
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)8
|
-10.16212
|
3.121996
|
55.38520
|
-3.255008
|
0.0019363
|
|
RVADYes
|
11.57901
|
3.999725
|
34.08235
|
2.894952
|
0.0065730
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
SexMale
|
-31.50752
|
9.007301
|
37.48747
|
-3.497997
|
0.0012251
|
|
SexMale:factor(Time)8
|
27.02760
|
8.735159
|
54.73131
|
3.094116
|
0.0031071
|
|
SexMale:factor(Time)3
|
24.44017
|
9.046112
|
55.34926
|
2.701732
|
0.0091385
|
|
factor(Time)8
|
-20.00903
|
7.482252
|
54.65772
|
-2.674199
|
0.0098594
|
|
factor(Time)3
|
-15.59653
|
7.482252
|
54.65772
|
-2.084470
|
0.0418040
|

IFN-a2
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
AgeGreater60older:factor(Time)5
|
-80.13405
|
27.57557
|
48.91764
|
-2.905980
|
0.0054855
|
|
AgeGreater60older
|
59.42952
|
25.37722
|
42.42564
|
2.341845
|
0.0239558
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)3
|
-27.99543
|
10.480918
|
56.66595
|
-2.671086
|
0.0098514
|
|
RVADYes:factor(Time)8
|
-20.33650
|
9.017817
|
55.39965
|
-2.255146
|
0.0280950
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Survivaldead:factor(Time)8
|
-21.73092
|
8.069861
|
55.34926
|
-2.692849
|
0.0093560
|
|
Survivaldead:factor(Time)3
|
-18.76413
|
8.701780
|
55.67109
|
-2.156355
|
0.0353913
|

CD19+CD5+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
VAD.IndicationDT:factor(Time)8
|
13.77699
|
5.156496
|
55.63322
|
2.671773
|
0.0098785
|
|
VAD.IndicationDT:factor(Time)21
|
18.57915
|
7.056032
|
57.34477
|
2.633087
|
0.0108560
|

IL-1b
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)8
|
7.942204
|
2.258115
|
47.94441
|
3.517183
|
0.0009652
|
|
LowIntermacsHigh:factor(Time)8
|
-8.641904
|
2.704957
|
47.76020
|
-3.194840
|
0.0024795
|
|
LowIntermacsHigh
|
5.410829
|
2.448628
|
37.85728
|
2.209739
|
0.0332453
|

G-CSF
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD27+IgD+IgM+ nonswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
OutcomeDied post OHT
|
51.75397
|
18.81024
|
34.01848
|
2.751372
|
0.0094435
|
|
OutcomeDied post OHT:factor(Time)8
|
-45.26825
|
18.42339
|
46.22234
|
-2.457108
|
0.0178180
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)3
|
-60.31567
|
14.86319
|
41.40883
|
-4.058057
|
0.0002141
|
|
factor(Time)8
|
-56.86000
|
14.86319
|
41.40883
|
-3.825558
|
0.0004327
|
|
factor(Time)5
|
-56.01533
|
14.86319
|
41.40883
|
-3.768729
|
0.0005127
|
|
OutcomeAlive s/p OHT:factor(Time)3
|
63.98437
|
17.82884
|
41.70621
|
3.588813
|
0.0008664
|
|
OutcomeAlive s/p OHT:factor(Time)5
|
63.40940
|
18.10497
|
42.00334
|
3.502320
|
0.0011083
|
|
OutcomeAlive s/p OHT
|
-52.98502
|
15.21731
|
40.22085
|
-3.481892
|
0.0012142
|
|
OutcomeDied:factor(Time)8
|
67.94475
|
19.66215
|
41.40883
|
3.455611
|
0.0012815
|
|
OutcomeDied:factor(Time)3
|
67.52042
|
19.66215
|
41.40883
|
3.434030
|
0.0013633
|
|
OutcomeAlive s/p OHT:factor(Time)8
|
60.98559
|
17.82884
|
41.70621
|
3.420615
|
0.0014097
|
|
OutcomeDied
|
-55.94450
|
16.90936
|
39.24878
|
-3.308493
|
0.0020156
|
|
OutcomeDied:factor(Time)5
|
57.59233
|
19.66215
|
41.40883
|
2.929096
|
0.0055066
|
|
factor(Time)1
|
-48.29333
|
16.88835
|
42.69337
|
-2.859564
|
0.0065379
|
|
OutcomeAlive s/p OHT:factor(Time)1
|
48.18291
|
19.55600
|
42.67658
|
2.463842
|
0.0178540
|
|
OutcomeDied post OHT
|
-62.76000
|
25.56455
|
39.24878
|
-2.454962
|
0.0186237
|
|
OutcomeDied:factor(Time)1
|
48.27333
|
21.23446
|
42.22333
|
2.273348
|
0.0281535
|

IL-8
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+27-38+CD5+transitionals
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

CD19+CD268+
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)14
|
-34.03871
|
8.813153
|
54.50836
|
-3.862263
|
0.0003005
|
|
AgeGreater60older:factor(Time)14
|
34.33335
|
10.627446
|
54.41277
|
3.230631
|
0.0020977
|
|
factor(Time)21
|
-17.19694
|
6.912037
|
54.73129
|
-2.487970
|
0.0159222
|
|
AgeGreater60older:factor(Time)3
|
19.32183
|
8.006711
|
54.60495
|
2.413204
|
0.0191972
|
|
factor(Time)8
|
-11.89000
|
5.511085
|
53.89410
|
-2.157470
|
0.0354451
|

CD27+IgD+ unswitched memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
RVADYes:factor(Time)8
|
-10.16212
|
3.121996
|
55.38520
|
-3.255008
|
0.0019363
|
|
RVADYes
|
11.57901
|
3.999725
|
34.08235
|
2.894952
|
0.0065730
|

Eotaxin
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

MIP-1a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
Survivaldead:factor(Time)3
|
32.83650
|
10.707160
|
48.95956
|
3.066780
|
0.0035187
|
|
factor(Time)3
|
-14.18310
|
6.214399
|
49.55959
|
-2.282297
|
0.0268003
|
|
Survivaldead:factor(Time)1
|
21.90146
|
10.827348
|
49.22900
|
2.022790
|
0.0485437
|

CD19+27+IgD-38++IgG ASC
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|

TNF-a
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
|
factor(Time)3
|
-37.21699
|
12.78570
|
50.77795
|
-2.910831
|
0.0053410
|
|
factor(Time)5
|
-37.50816
|
13.62272
|
52.06253
|
-2.753353
|
0.0081034
|
|
factor(Time)8
|
-37.45827
|
13.62272
|
52.06253
|
-2.749690
|
0.0081827
|
|
SexMale:factor(Time)8
|
41.33081
|
15.82776
|
51.47185
|
2.611286
|
0.0117898
|
|
SexMale:factor(Time)3
|
39.58387
|
15.23657
|
50.60393
|
2.597952
|
0.0122521
|
|
factor(Time)1
|
-32.66139
|
12.78570
|
50.77795
|
-2.554526
|
0.0136757
|
|
SexMale:factor(Time)5
|
39.72646
|
15.94812
|
51.60286
|
2.490980
|
0.0159951
|
|
SexMale
|
-25.81619
|
12.80567
|
54.31830
|
-2.015997
|
0.0487575
|

CD19CD24hiCD38-memory
|
|
Estimate
|
Std. Error
|
df
|
t value
|
Pr(>|t|)
|
